An analysis of various diseases have been predicted using multiple data mining and text mining techniques. In this article we are going to discuss about 6 prediction techniques. Using gene expression pattern we predict the disease outcome and implementation of pathway based approach for classifying disease based on hyper box principles, we also present a novel hybrid prediction model with missing value imputation HPM-MI which analyze imputation using simple k-means clustering. A technique based on CCAR Constraint Class Association Rule has been used for reducing time consumption in prediction of a particular disease. We have discussed about text mining technique and their applications. Another technique has also been studied about hyper triglyceride mia from anthropometric measures which diverge according to age and gender. Using multilayer classifiers for disease prediction we can achieve high diagnosis accuracy and high performance. C. Leancy Jannet | G. V. Sumalatha "A Survey on Various Disease Prediction Techniques" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-6 , October 2018, URL: http://www.ijtsrd.com/papers/ijtsrd18624.pdf
GRAPHICAL MODEL AND CLUSTERINGREGRESSION BASED METHODS FOR CAUSAL INTERACTION...ijaia
The early detection of Breast Cancer, the deadly disease that mostly affects women is extremely complex because it requires various features of the cell type. Therefore, the efficient approach to diagnosing Breast Cancer at the early stage was to apply artificial intelligence where machines are simulated with intelligence and programmed to think and act like a human. This allows machines to passively learn and find a pattern, which can be used later to detect any new changes that may occur. In general, machine learning is quite useful particularly in the medical field, which depends on complex genomic measurements such as microarray technique and would increase the accuracy and precision of results. With this technology, doctors can easily diagnose patients with cancer quickly and apply the proper treatment in a timely manner. Therefore, the goal of this paper is to address and propose a robust Breast Cancer diagnostic system using complex genomic analysis via microarray technology. The system will combine two machine learning methods, K-means cluster, and linear regression.
Evaluation of Logistic Regression and Neural Network Model With Sensitivity A...CSCJournals
Logistic Regression (LR) is a well known classification method in the field of statistical learning. It allows probabilistic classification and shows promising results on several benchmark problems. Logistic regression enables us to investigate the relationship between a categorical outcome and a set of explanatory variables. Artificial Neural Networks (ANNs) are popularly used as universal non-linear inference models and have gained extensive popularity in recent years. Research activities are considerable and literature is growing. The goal of this research work is to compare the performance of Logistic Regression and Neural Network models on publicly available medical datasets. The evaluation process of the model is as follows. The logistic regression and neural network methods with sensitivity analysis have been evaluated for the effectiveness of the classification. The Classification Accuracy is used to measure the performance of both the models. From the experimental results it is confirmed that the neural network model with sensitivity analysis model gives more efficient result.
Zen and the Art of Data Science MaintenanceElsevier
Dr. Jabe Wilson, Elsevier's Consulting Director of Text and Data Analytics, gave this presentation at the Bio-IT World Conference in Boston on May 17, 2018.
Hybrid prediction model with missing value imputation for medical data 2015-g...Jitender Grover
The document presents a novel hybrid prediction model called HPM-MI that uses K-means clustering and multilayer perceptron (MLP) to improve predictive classification for medical data with missing values. The model first analyzes 11 different imputation techniques using K-means clustering to select the best one for filling missing values in the data. It then uses K-means clustering again to validate class labels and remove incorrectly classified instances before applying the MLP classifier. The model is tested on three medical datasets from the UCI repository and shows improved accuracy, sensitivity, specificity and other metrics compared to existing methods, particularly when datasets have large numbers of missing values.
Big data analytics is being used in healthcare and disease management to gain knowledge and insights from large datasets. Hypothesis validation and creation can lead to knowledge when applying big data techniques. Causal relationships between variables can be identified from data to understand the causes of events and alter outcomes. However, correlation does not always indicate causation, and confounding factors must be considered. Genomics data in particular poses challenges due to its large volume, velocity, variety and other attributes, but can be applied from research to precision medicine through genetic testing.
Discuss about Al, machine learning, and the hype cycle
Discuss the knowledge-based classification of proteins
Discuss applications of AI/ML to drug discovery
Efficiency of Prediction Algorithms for Mining Biological DatabasesIOSR Journals
This document analyzes the efficiency of various prediction algorithms for mining biological databases. It discusses prediction through mining biological databases to identify disease risks. It then evaluates several prediction algorithms (ZeroR, OneR, JRip, PART, Decision Table) on a breast cancer dataset using measures like accuracy, sensitivity, specificity, and predictive values. The results show that the JRip and PART algorithms generally had the highest accuracy rates, around 70%, while ZeroR had the lowest accuracy. However, ZeroR had a perfect positive predictive value. The study aims to assess the most efficient algorithms for predictive mining of biological data.
GRAPHICAL MODEL AND CLUSTERINGREGRESSION BASED METHODS FOR CAUSAL INTERACTION...ijaia
The early detection of Breast Cancer, the deadly disease that mostly affects women is extremely complex because it requires various features of the cell type. Therefore, the efficient approach to diagnosing Breast Cancer at the early stage was to apply artificial intelligence where machines are simulated with intelligence and programmed to think and act like a human. This allows machines to passively learn and find a pattern, which can be used later to detect any new changes that may occur. In general, machine learning is quite useful particularly in the medical field, which depends on complex genomic measurements such as microarray technique and would increase the accuracy and precision of results. With this technology, doctors can easily diagnose patients with cancer quickly and apply the proper treatment in a timely manner. Therefore, the goal of this paper is to address and propose a robust Breast Cancer diagnostic system using complex genomic analysis via microarray technology. The system will combine two machine learning methods, K-means cluster, and linear regression.
Evaluation of Logistic Regression and Neural Network Model With Sensitivity A...CSCJournals
Logistic Regression (LR) is a well known classification method in the field of statistical learning. It allows probabilistic classification and shows promising results on several benchmark problems. Logistic regression enables us to investigate the relationship between a categorical outcome and a set of explanatory variables. Artificial Neural Networks (ANNs) are popularly used as universal non-linear inference models and have gained extensive popularity in recent years. Research activities are considerable and literature is growing. The goal of this research work is to compare the performance of Logistic Regression and Neural Network models on publicly available medical datasets. The evaluation process of the model is as follows. The logistic regression and neural network methods with sensitivity analysis have been evaluated for the effectiveness of the classification. The Classification Accuracy is used to measure the performance of both the models. From the experimental results it is confirmed that the neural network model with sensitivity analysis model gives more efficient result.
Zen and the Art of Data Science MaintenanceElsevier
Dr. Jabe Wilson, Elsevier's Consulting Director of Text and Data Analytics, gave this presentation at the Bio-IT World Conference in Boston on May 17, 2018.
Hybrid prediction model with missing value imputation for medical data 2015-g...Jitender Grover
The document presents a novel hybrid prediction model called HPM-MI that uses K-means clustering and multilayer perceptron (MLP) to improve predictive classification for medical data with missing values. The model first analyzes 11 different imputation techniques using K-means clustering to select the best one for filling missing values in the data. It then uses K-means clustering again to validate class labels and remove incorrectly classified instances before applying the MLP classifier. The model is tested on three medical datasets from the UCI repository and shows improved accuracy, sensitivity, specificity and other metrics compared to existing methods, particularly when datasets have large numbers of missing values.
Big data analytics is being used in healthcare and disease management to gain knowledge and insights from large datasets. Hypothesis validation and creation can lead to knowledge when applying big data techniques. Causal relationships between variables can be identified from data to understand the causes of events and alter outcomes. However, correlation does not always indicate causation, and confounding factors must be considered. Genomics data in particular poses challenges due to its large volume, velocity, variety and other attributes, but can be applied from research to precision medicine through genetic testing.
Discuss about Al, machine learning, and the hype cycle
Discuss the knowledge-based classification of proteins
Discuss applications of AI/ML to drug discovery
Efficiency of Prediction Algorithms for Mining Biological DatabasesIOSR Journals
This document analyzes the efficiency of various prediction algorithms for mining biological databases. It discusses prediction through mining biological databases to identify disease risks. It then evaluates several prediction algorithms (ZeroR, OneR, JRip, PART, Decision Table) on a breast cancer dataset using measures like accuracy, sensitivity, specificity, and predictive values. The results show that the JRip and PART algorithms generally had the highest accuracy rates, around 70%, while ZeroR had the lowest accuracy. However, ZeroR had a perfect positive predictive value. The study aims to assess the most efficient algorithms for predictive mining of biological data.
The Evaluated Measurement of a Combined Genetic Algorithm and Artificial Immu...IJECEIAES
This paper demonstrates a hybrid between two optimization methods which are the Artificial Immune System (AIS) and Genetic Algorithm (GA). The novel algorithm called the immune genetic algorithm (IGA), provides improvement to the results that enable GA and AIS to work separately which is the main objective of this hybrid. Negative selection which is one of the techniques in the AIS, was employed to determine the input variables (populations) of the system. In order to illustrate the effectiveness of the IGA, the comparison with a steady-state GA, AIS, and PSO were also investigated. The testing of the performance was conducted by mathematical testing, problems were divided into single and multiple objectives. The five single objectives were then used to test the modified algorithm, the results showed that IGA performed better than all of the other methods. The DTLZ multi-objective testing functions were then used. The result also illustrated that the modified approach still had the best performance.
Simplified Knowledge Prediction: Application of Machine Learning in Real LifePeea Bal Chakraborty
Machine learning is the scientific study of algorithms and statistical models that is used by the machines to perform a specific task depending on patterns and inference rather than explicit instructions. This research and analysis aims to observe how precisely a machine can predict that a patient suspected of breast cancer is having malignant or benign cancer.In this paper the classification of cancer type and prediction of risk levels is done by various model of machine learning and is pictorially depicted by various tools of visual analytics.
ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY "AN OVERVIEW OF AWARENESS"FinianCN
ARTIFICIAL INTELLIGENT IN DRUG DISCOVERY:- AN OVERVIEW OF AWARENESS.
AI is showing the potential to be a faster and more efficient way to find and develop new drugs. A growing number of organizations and universities are focusing to minimize the complexities involved in the classical way of drug discovery by using AI computing to envisage which drug candidate are most likely to be effective treatments.
It is hard to measure the adoption of AI in drug discovery. Pharma and biotech companies tend to not publicly disclose competitive technology use.
While organizations are adopting the technology, there is significant untapped potential for those willing to be more aggressive. Which is depending on the realization of the potential with education and relevant success stories
This document discusses the use of artificial intelligence in drug discovery and development. It begins by defining artificial intelligence, machine learning, and deep learning. It then provides examples of how AI is currently used in various stages of the drug development process, including identifying molecular targets, finding hit compounds, optimizing lead compounds, predicting toxicity, and drug repurposing. It also discusses startups applying AI to drug discovery. Finally, it notes some limitations and drawbacks of using AI, such as potential bias in algorithms.
Cancer prognosis prediction using balanced stratified samplingijscai
High accuracy in cancer prediction is important to improve the quality of the treatment and to improve the
rate of survivability of patients. As the data volume is increasing rapidly in the healthcare research, the
analytical challenge exists in double. The use of effective sampling technique in classification algorithms
always yields good prediction accuracy. The SEER public use cancer database provides various prominent
class labels for prognosis prediction. The main objective of this paper is to find the effect of sampling
techniques in classifying the prognosis variable and propose an ideal sampling method based on the
outcome of the experimentation. In the first phase of this work the traditional random sampling and
stratified sampling techniques have been used. At the next level the balanced stratified sampling with
variations as per the choice of the prognosis class labels have been tested. Much of the initial time has been
focused on performing the pre-processing of the SEER data set. The classification model for
experimentation has been built using the breast cancer, respiratory cancer and mixed cancer data sets with
three traditional classifiers namely Decision Tree, Naïve Bayes and K-Nearest Neighbour. The three
prognosis factors survival, stage and metastasis have been used as class labels for experimental
comparisons. The results shows a steady increase in the prediction accuracy of balanced stratified model
as the sample size increases, but the traditional approach fluctuates before the optimum results.
Artificial intelligence techniques are being used in drug discovery and development to identify novel drug-target interactions, infer the functional importance of cellular pathways, and overcome challenges in traditional methods. AI approaches leverage large datasets and complex algorithms to extract meaningful insights in ways that are not limited by existing knowledge. This helps identify compounds that could bind to previously "undruggable" targets. Potential advantages of AI include reduced subjective bias, high predictive value, and ability to analyze targets not easily addressed by traditional methods. Drawbacks include risks of algorithm bias, challenges in result interpretation, validation needs, costs, and technical issues.
Estimating the Statistical Significance of Classifiers used in the Predictio...IOSR Journals
This document summarizes a research paper that analyzes the statistical significance of different classifiers for predicting tuberculosis. The paper first compares the accuracy of classifiers like decision trees, support vector machines, k-nearest neighbor, and naive Bayes on tuberculosis data. It then evaluates the performance of these classifiers using a paired t-test to select the optimal model. The results showed that support vector machines and decision trees were not statistically significant, while support vector machines combined with naive Bayes and k-nearest neighbor were statistically significant.
The state of the art in behavioral machine learning for healthcareAfrica Perianez
The use of smart devices and wearables is becoming increasingly popular. This allows patients to be continuously monitored and provides a huge amount of health-related data that, if properly analyzed, can be used to improve their health by predicting potential future conditions. Advanced machine learning techniques do permit such analysis, and thus serve to forecast the evolution and health challenges of individual patients. This includes, for instance, issues as critical as early detection of heart disease.
But, moreover, the whole healthcare sector is currently undergoing a profound transformation. The rich profusion of digital data is fostering a move from more traditional approaches towards a data-driven prevention model.
In this talk I survey state of the art methods that allowed an AI-based early diagnosis and risk assessment for individual patients, using information that may include health records, genomic and wearable device data, medical imagery and online physician reviews. I will focus on methods that can be employed to forecast future events affecting a specific patient and serve to evaluate wearable device data and assist healthcare industry in undertaking a patient-focused data-driven preventive approach. Additionally, I introduce how machine-learning-based gamification techniques can be employed to motivate individual users to improve their health condition and achieve personalized challenges.
Genome-wide transcription profiling is a powerful technique in studying disease susceptible footprints. Moreover, when applied to disease tissue it may reveal quantitative and qualitative alterations in gene expression that give information on the context or underlying basis for the disease and may provide a new diagnostic approach. However, the data obtained from high-density microarrays is highly complex and poses considerable challenges in data mining. Past researches prove that neuro diseases damage the brain network interaction, protein- protein interaction and gene-gene interaction. A number of neurological research paper also analyze the relationship among damaged part. Analysis of gene-gene interaction network drawn by using state-of-the-art gene database of Alzheimer’s patient can conclude a lot of information. In this paper we used gene dataset affected with Alzheimer’s disease and normal patient’s dataset from NCBI databank. After proper processing the .CEL affymetrix data using RMA, we use the processed data to find gene interaction outputs. Then we filter the output files using probe set filtering attributes p-value and fold count and draw a gene-gene interaction network. Then we analyze the interaction network using GeneMania software.
Prediction & Survival Rate Prostate Cancer Patient using Artificial Neural Ne...rahulmonikasharma
This document summarizes a research paper that aims to develop an artificial neural network model to improve prostate cancer detection and prediction of patient survival rates. The researchers propose using a multilayer perceptron neural network with inputs like PSA levels, prostate volume and digital rectal exam results to reduce the number of false positive prostate cancer diagnoses compared to current screening methods. They have developed a mathematical model and are working on a software application to implement the model for use in cancer screening centers, hospitals and research organizations. The goal is to build a more accurate system that can help detect prostate cancer earlier and reduce unnecessary medical procedures.
Computational approaches using AI are being used to speed up drug discovery and clinical trials in the following ways:
(1) AI is being applied to large datasets to help identify new biomarkers and repurpose existing drugs, with the global AI healthcare market expected to reach $36.1 billion by 2025.
(2) Major pharmaceutical companies are collaborating and sharing data using AI to accelerate target identification and automate molecule design.
(3) Startups are generating huge image datasets from high-throughput drug screening experiments to help identify new drug candidates in areas like oncology.
(4) AI can help improve clinical trials by identifying best patient populations, enabling dynamic trial design adjustments, and improving patient access and
Artificial intelligence (AI) has the potential to transform drug discovery through the use of semantic networks to represent biomedical knowledge as facts and infer new insights, probabilistic rules to evaluate beliefs about candidate drugs, and optimization and simulation techniques to iteratively improve outcomes. By continuously learning from diverse sources of data, AI systems could automate and enhance the processes of target identification, compound screening and testing in ways that accelerate research and development compared to traditional analytical tools.
In order to cope with real-world problems more effectively, we tend to design a decision support system for tuberculosis bacterium class identification. In this paper, we are concerned to propose a fuzzy diagnosability approach, which takes value between {0, 1} and based on observability of events, we
formalized the construction of diagnoses that are used to perform diagnosis. In particular, we present a
framework of the fuzzy expert system; discuss the suitability of artificial intelligence as a novel soft paradigm and reviews work from the literature for the development of a medical diagnostic system. The newly proposed approach allows us to deal with problems of diagnosability for both crisp and fuzzy value of input data. Accuracy analysis of designed decision support system based on demographic data was done
by comparing expert knowledge and system generated response. This basic emblematic approach using
fuzzy inference system is presented that describes a technique to forecast the existence of bacterium and
provides support platform to pulmonary researchers in identifying the ailment effectively.
Technology and data-driven approaches are helping advance our understanding of science and medicine. Deep learning algorithms can analyze large amounts of medical data to identify patterns and make diagnoses. One study found that a deep learning system correctly identified the diagnosis of skin lesions 72% of the time, outperforming board-certified dermatologists who were correct 66% of the time. Initiatives like the Cancer Moonshot aim to apply these types of data-driven approaches and machine learning to make breakthroughs in cancer research, with a goal of accelerating progress by 10 years. These new technologies have potential to transform how doctors learn, practice medicine, and improve patient outcomes.
Clustering Approaches for Evaluation and Analysis on Formal Gene Expression C...rahulmonikasharma
Enormous generation of biological data and the need of analysis of that data led to the generation of the field Bioinformatics. Data mining is the stream which is used to derive, analyze the data by exploring the hidden patterns of the biological data. Though, data mining can be used in analyzing biological data such as genomic data, proteomic data here Gene Expression (GE) Data is considered for evaluation. GE is generated from Microarrays such as DNA and oligo micro arrays. The generated data is analyzed through the clustering techniques of data mining. This study deals with an implement the basic clustering approach K-Means and various clustering approaches like Hierarchal, Som, Click and basic fuzzy based clustering approach. Eventually, the comparative study of those approaches which lead to the effective approach of cluster analysis of GE.The experimental results shows that proposed algorithm achieve a higher clustering accuracy and takes less clustering time when compared with existing algorithms.
Improving the effectiveness of information retrieval system using adaptive ge...ijcsit
The document describes research into improving the effectiveness of information retrieval systems using an adaptive genetic algorithm. A genetic algorithm with variable crossover and mutation probabilities (adaptive GA) is investigated. The adaptive GA is tested on 242 Arabic abstracts using three information retrieval models: vector space model, extended Boolean model, and language model. Results show the adaptive GA approach improves retrieval effectiveness over traditional genetic algorithms and baseline information retrieval systems, as measured by average recall and precision. Key aspects of the adaptive GA used include variable crossover and mutation probabilities tuned during the search process, and fitness functions based on document retrieval order.
DISEASE PREDICTION BY MACHINE LEARNING OVER BIG DATA FROM HEALTHCARE COMMUNI...Nexgen Technology
This document proposes a new convolutional neural network based multimodal disease risk prediction algorithm (CNN-MDRP) that uses both structured and unstructured data from healthcare to improve disease prediction accuracy. It aims to address challenges from incomplete medical data and regional differences in diseases. The algorithm reconstructs missing data, identifies major regional chronic diseases, extracts useful features from structured and unstructured text data using CNN, and combines these for risk prediction. Experimental results show the CNN-MDRP approach achieves 94.8% accuracy, faster than existing CNN-based methods that only use single data types.
Graphical Model and Clustering-Regression based Methods for Causal Interactio...gerogepatton
The early detection of Breast Cancer, the deadly disease that mostly affects women is extremely complex
because it requires various features of the cell type. Therefore, the efficient approach to diagnosing Breast
Cancer at the early stage was to apply artificial intelligence where machines are simulated with
intelligence and programmed to think and act like a human. This allows machines to passively learn and
find a pattern, which can be used later to detect any new changes that may occur. In general, machine
learning is quite useful particularly in the medical field, which depends on complex genomic
measurements such as microarray technique and would increase the accuracy and precision of results.
With this technology, doctors can easily diagnose patients with cancer quickly and apply the proper
treatment in a timely manner. Therefore, the goal of this paper is to address and propose a robust Breast
Cancer diagnostic system using complex genomic analysis via microarray technology. The system will
combine two machine learning methods, K-means cluster, and linear regression.
GRAPHICAL MODEL AND CLUSTERINGREGRESSION BASED METHODS FOR CAUSAL INTERACTION...gerogepatton
The early detection of Breast Cancer, the deadly disease that mostly affects women is extremely complex because it requires various features of the cell type. Therefore, the efficient approach to diagnosing Breast Cancer at the early stage was to apply artificial intelligence where machines are simulated with intelligence and programmed to think and act like a human. This allows machines to passively learn and find a pattern, which can be used later to detect any new changes that may occur. In general, machine learning is quite useful particularly in the medical field, which depends on complex genomic measurements such as microarray technique and would increase the accuracy and precision of results. With this technology, doctors can easily diagnose patients with cancer quickly and apply the proper treatment in a timely manner. Therefore, the goal of this paper is to address and propose a robust Breast Cancer diagnostic system using complex genomic analysis via microarray technology. The system will combine two machine learning methods, K-means cluster, and linear regression.
This document describes an advanced machine learning approach for predicting skin cancer. It discusses using machine learning algorithms like Naive Bayes, Decision Tree, Random Forest on a dataset to estimate disease risk and determine algorithm accuracy. The paper focuses on developing a system that integrates symptom and medical data using machine learning algorithms like K-means to provide accurate disease predictions.
Prediction, Big Data, and AI: Steyerberg, Basel Nov 1, 2019Ewout Steyerberg
Title"Clinical prediction models in the age of artificial intelligence and big data", presented at the Basel Biometrics Society seminar Nov 1, 2019, Basel, by Ewout Steyerberg, with substantial inout from Maarten van Smeden and Ben van Calster
This document is a research statement by Chien-Wei (Masaki) Lin that summarizes his past and ongoing methodology and collaborative research projects. It discusses his interests in developing statistical methods for analyzing multi-omics data, including power calculation tools, meta-analysis and integrative analysis methods. It also summarizes some of Lin's collaboration projects applying these statistical methods to study topics like brain aging, major depressive disorder, and cardiovascular epidemiology. The document references 18 of Lin's publications and provides an overview of his diverse experience and future research plans developing statistical tools and methods and applying them to biological problems.
The Evaluated Measurement of a Combined Genetic Algorithm and Artificial Immu...IJECEIAES
This paper demonstrates a hybrid between two optimization methods which are the Artificial Immune System (AIS) and Genetic Algorithm (GA). The novel algorithm called the immune genetic algorithm (IGA), provides improvement to the results that enable GA and AIS to work separately which is the main objective of this hybrid. Negative selection which is one of the techniques in the AIS, was employed to determine the input variables (populations) of the system. In order to illustrate the effectiveness of the IGA, the comparison with a steady-state GA, AIS, and PSO were also investigated. The testing of the performance was conducted by mathematical testing, problems were divided into single and multiple objectives. The five single objectives were then used to test the modified algorithm, the results showed that IGA performed better than all of the other methods. The DTLZ multi-objective testing functions were then used. The result also illustrated that the modified approach still had the best performance.
Simplified Knowledge Prediction: Application of Machine Learning in Real LifePeea Bal Chakraborty
Machine learning is the scientific study of algorithms and statistical models that is used by the machines to perform a specific task depending on patterns and inference rather than explicit instructions. This research and analysis aims to observe how precisely a machine can predict that a patient suspected of breast cancer is having malignant or benign cancer.In this paper the classification of cancer type and prediction of risk levels is done by various model of machine learning and is pictorially depicted by various tools of visual analytics.
ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY "AN OVERVIEW OF AWARENESS"FinianCN
ARTIFICIAL INTELLIGENT IN DRUG DISCOVERY:- AN OVERVIEW OF AWARENESS.
AI is showing the potential to be a faster and more efficient way to find and develop new drugs. A growing number of organizations and universities are focusing to minimize the complexities involved in the classical way of drug discovery by using AI computing to envisage which drug candidate are most likely to be effective treatments.
It is hard to measure the adoption of AI in drug discovery. Pharma and biotech companies tend to not publicly disclose competitive technology use.
While organizations are adopting the technology, there is significant untapped potential for those willing to be more aggressive. Which is depending on the realization of the potential with education and relevant success stories
This document discusses the use of artificial intelligence in drug discovery and development. It begins by defining artificial intelligence, machine learning, and deep learning. It then provides examples of how AI is currently used in various stages of the drug development process, including identifying molecular targets, finding hit compounds, optimizing lead compounds, predicting toxicity, and drug repurposing. It also discusses startups applying AI to drug discovery. Finally, it notes some limitations and drawbacks of using AI, such as potential bias in algorithms.
Cancer prognosis prediction using balanced stratified samplingijscai
High accuracy in cancer prediction is important to improve the quality of the treatment and to improve the
rate of survivability of patients. As the data volume is increasing rapidly in the healthcare research, the
analytical challenge exists in double. The use of effective sampling technique in classification algorithms
always yields good prediction accuracy. The SEER public use cancer database provides various prominent
class labels for prognosis prediction. The main objective of this paper is to find the effect of sampling
techniques in classifying the prognosis variable and propose an ideal sampling method based on the
outcome of the experimentation. In the first phase of this work the traditional random sampling and
stratified sampling techniques have been used. At the next level the balanced stratified sampling with
variations as per the choice of the prognosis class labels have been tested. Much of the initial time has been
focused on performing the pre-processing of the SEER data set. The classification model for
experimentation has been built using the breast cancer, respiratory cancer and mixed cancer data sets with
three traditional classifiers namely Decision Tree, Naïve Bayes and K-Nearest Neighbour. The three
prognosis factors survival, stage and metastasis have been used as class labels for experimental
comparisons. The results shows a steady increase in the prediction accuracy of balanced stratified model
as the sample size increases, but the traditional approach fluctuates before the optimum results.
Artificial intelligence techniques are being used in drug discovery and development to identify novel drug-target interactions, infer the functional importance of cellular pathways, and overcome challenges in traditional methods. AI approaches leverage large datasets and complex algorithms to extract meaningful insights in ways that are not limited by existing knowledge. This helps identify compounds that could bind to previously "undruggable" targets. Potential advantages of AI include reduced subjective bias, high predictive value, and ability to analyze targets not easily addressed by traditional methods. Drawbacks include risks of algorithm bias, challenges in result interpretation, validation needs, costs, and technical issues.
Estimating the Statistical Significance of Classifiers used in the Predictio...IOSR Journals
This document summarizes a research paper that analyzes the statistical significance of different classifiers for predicting tuberculosis. The paper first compares the accuracy of classifiers like decision trees, support vector machines, k-nearest neighbor, and naive Bayes on tuberculosis data. It then evaluates the performance of these classifiers using a paired t-test to select the optimal model. The results showed that support vector machines and decision trees were not statistically significant, while support vector machines combined with naive Bayes and k-nearest neighbor were statistically significant.
The state of the art in behavioral machine learning for healthcareAfrica Perianez
The use of smart devices and wearables is becoming increasingly popular. This allows patients to be continuously monitored and provides a huge amount of health-related data that, if properly analyzed, can be used to improve their health by predicting potential future conditions. Advanced machine learning techniques do permit such analysis, and thus serve to forecast the evolution and health challenges of individual patients. This includes, for instance, issues as critical as early detection of heart disease.
But, moreover, the whole healthcare sector is currently undergoing a profound transformation. The rich profusion of digital data is fostering a move from more traditional approaches towards a data-driven prevention model.
In this talk I survey state of the art methods that allowed an AI-based early diagnosis and risk assessment for individual patients, using information that may include health records, genomic and wearable device data, medical imagery and online physician reviews. I will focus on methods that can be employed to forecast future events affecting a specific patient and serve to evaluate wearable device data and assist healthcare industry in undertaking a patient-focused data-driven preventive approach. Additionally, I introduce how machine-learning-based gamification techniques can be employed to motivate individual users to improve their health condition and achieve personalized challenges.
Genome-wide transcription profiling is a powerful technique in studying disease susceptible footprints. Moreover, when applied to disease tissue it may reveal quantitative and qualitative alterations in gene expression that give information on the context or underlying basis for the disease and may provide a new diagnostic approach. However, the data obtained from high-density microarrays is highly complex and poses considerable challenges in data mining. Past researches prove that neuro diseases damage the brain network interaction, protein- protein interaction and gene-gene interaction. A number of neurological research paper also analyze the relationship among damaged part. Analysis of gene-gene interaction network drawn by using state-of-the-art gene database of Alzheimer’s patient can conclude a lot of information. In this paper we used gene dataset affected with Alzheimer’s disease and normal patient’s dataset from NCBI databank. After proper processing the .CEL affymetrix data using RMA, we use the processed data to find gene interaction outputs. Then we filter the output files using probe set filtering attributes p-value and fold count and draw a gene-gene interaction network. Then we analyze the interaction network using GeneMania software.
Prediction & Survival Rate Prostate Cancer Patient using Artificial Neural Ne...rahulmonikasharma
This document summarizes a research paper that aims to develop an artificial neural network model to improve prostate cancer detection and prediction of patient survival rates. The researchers propose using a multilayer perceptron neural network with inputs like PSA levels, prostate volume and digital rectal exam results to reduce the number of false positive prostate cancer diagnoses compared to current screening methods. They have developed a mathematical model and are working on a software application to implement the model for use in cancer screening centers, hospitals and research organizations. The goal is to build a more accurate system that can help detect prostate cancer earlier and reduce unnecessary medical procedures.
Computational approaches using AI are being used to speed up drug discovery and clinical trials in the following ways:
(1) AI is being applied to large datasets to help identify new biomarkers and repurpose existing drugs, with the global AI healthcare market expected to reach $36.1 billion by 2025.
(2) Major pharmaceutical companies are collaborating and sharing data using AI to accelerate target identification and automate molecule design.
(3) Startups are generating huge image datasets from high-throughput drug screening experiments to help identify new drug candidates in areas like oncology.
(4) AI can help improve clinical trials by identifying best patient populations, enabling dynamic trial design adjustments, and improving patient access and
Artificial intelligence (AI) has the potential to transform drug discovery through the use of semantic networks to represent biomedical knowledge as facts and infer new insights, probabilistic rules to evaluate beliefs about candidate drugs, and optimization and simulation techniques to iteratively improve outcomes. By continuously learning from diverse sources of data, AI systems could automate and enhance the processes of target identification, compound screening and testing in ways that accelerate research and development compared to traditional analytical tools.
In order to cope with real-world problems more effectively, we tend to design a decision support system for tuberculosis bacterium class identification. In this paper, we are concerned to propose a fuzzy diagnosability approach, which takes value between {0, 1} and based on observability of events, we
formalized the construction of diagnoses that are used to perform diagnosis. In particular, we present a
framework of the fuzzy expert system; discuss the suitability of artificial intelligence as a novel soft paradigm and reviews work from the literature for the development of a medical diagnostic system. The newly proposed approach allows us to deal with problems of diagnosability for both crisp and fuzzy value of input data. Accuracy analysis of designed decision support system based on demographic data was done
by comparing expert knowledge and system generated response. This basic emblematic approach using
fuzzy inference system is presented that describes a technique to forecast the existence of bacterium and
provides support platform to pulmonary researchers in identifying the ailment effectively.
Technology and data-driven approaches are helping advance our understanding of science and medicine. Deep learning algorithms can analyze large amounts of medical data to identify patterns and make diagnoses. One study found that a deep learning system correctly identified the diagnosis of skin lesions 72% of the time, outperforming board-certified dermatologists who were correct 66% of the time. Initiatives like the Cancer Moonshot aim to apply these types of data-driven approaches and machine learning to make breakthroughs in cancer research, with a goal of accelerating progress by 10 years. These new technologies have potential to transform how doctors learn, practice medicine, and improve patient outcomes.
Clustering Approaches for Evaluation and Analysis on Formal Gene Expression C...rahulmonikasharma
Enormous generation of biological data and the need of analysis of that data led to the generation of the field Bioinformatics. Data mining is the stream which is used to derive, analyze the data by exploring the hidden patterns of the biological data. Though, data mining can be used in analyzing biological data such as genomic data, proteomic data here Gene Expression (GE) Data is considered for evaluation. GE is generated from Microarrays such as DNA and oligo micro arrays. The generated data is analyzed through the clustering techniques of data mining. This study deals with an implement the basic clustering approach K-Means and various clustering approaches like Hierarchal, Som, Click and basic fuzzy based clustering approach. Eventually, the comparative study of those approaches which lead to the effective approach of cluster analysis of GE.The experimental results shows that proposed algorithm achieve a higher clustering accuracy and takes less clustering time when compared with existing algorithms.
Improving the effectiveness of information retrieval system using adaptive ge...ijcsit
The document describes research into improving the effectiveness of information retrieval systems using an adaptive genetic algorithm. A genetic algorithm with variable crossover and mutation probabilities (adaptive GA) is investigated. The adaptive GA is tested on 242 Arabic abstracts using three information retrieval models: vector space model, extended Boolean model, and language model. Results show the adaptive GA approach improves retrieval effectiveness over traditional genetic algorithms and baseline information retrieval systems, as measured by average recall and precision. Key aspects of the adaptive GA used include variable crossover and mutation probabilities tuned during the search process, and fitness functions based on document retrieval order.
DISEASE PREDICTION BY MACHINE LEARNING OVER BIG DATA FROM HEALTHCARE COMMUNI...Nexgen Technology
This document proposes a new convolutional neural network based multimodal disease risk prediction algorithm (CNN-MDRP) that uses both structured and unstructured data from healthcare to improve disease prediction accuracy. It aims to address challenges from incomplete medical data and regional differences in diseases. The algorithm reconstructs missing data, identifies major regional chronic diseases, extracts useful features from structured and unstructured text data using CNN, and combines these for risk prediction. Experimental results show the CNN-MDRP approach achieves 94.8% accuracy, faster than existing CNN-based methods that only use single data types.
Graphical Model and Clustering-Regression based Methods for Causal Interactio...gerogepatton
The early detection of Breast Cancer, the deadly disease that mostly affects women is extremely complex
because it requires various features of the cell type. Therefore, the efficient approach to diagnosing Breast
Cancer at the early stage was to apply artificial intelligence where machines are simulated with
intelligence and programmed to think and act like a human. This allows machines to passively learn and
find a pattern, which can be used later to detect any new changes that may occur. In general, machine
learning is quite useful particularly in the medical field, which depends on complex genomic
measurements such as microarray technique and would increase the accuracy and precision of results.
With this technology, doctors can easily diagnose patients with cancer quickly and apply the proper
treatment in a timely manner. Therefore, the goal of this paper is to address and propose a robust Breast
Cancer diagnostic system using complex genomic analysis via microarray technology. The system will
combine two machine learning methods, K-means cluster, and linear regression.
GRAPHICAL MODEL AND CLUSTERINGREGRESSION BASED METHODS FOR CAUSAL INTERACTION...gerogepatton
The early detection of Breast Cancer, the deadly disease that mostly affects women is extremely complex because it requires various features of the cell type. Therefore, the efficient approach to diagnosing Breast Cancer at the early stage was to apply artificial intelligence where machines are simulated with intelligence and programmed to think and act like a human. This allows machines to passively learn and find a pattern, which can be used later to detect any new changes that may occur. In general, machine learning is quite useful particularly in the medical field, which depends on complex genomic measurements such as microarray technique and would increase the accuracy and precision of results. With this technology, doctors can easily diagnose patients with cancer quickly and apply the proper treatment in a timely manner. Therefore, the goal of this paper is to address and propose a robust Breast Cancer diagnostic system using complex genomic analysis via microarray technology. The system will combine two machine learning methods, K-means cluster, and linear regression.
This document describes an advanced machine learning approach for predicting skin cancer. It discusses using machine learning algorithms like Naive Bayes, Decision Tree, Random Forest on a dataset to estimate disease risk and determine algorithm accuracy. The paper focuses on developing a system that integrates symptom and medical data using machine learning algorithms like K-means to provide accurate disease predictions.
Prediction, Big Data, and AI: Steyerberg, Basel Nov 1, 2019Ewout Steyerberg
Title"Clinical prediction models in the age of artificial intelligence and big data", presented at the Basel Biometrics Society seminar Nov 1, 2019, Basel, by Ewout Steyerberg, with substantial inout from Maarten van Smeden and Ben van Calster
This document is a research statement by Chien-Wei (Masaki) Lin that summarizes his past and ongoing methodology and collaborative research projects. It discusses his interests in developing statistical methods for analyzing multi-omics data, including power calculation tools, meta-analysis and integrative analysis methods. It also summarizes some of Lin's collaboration projects applying these statistical methods to study topics like brain aging, major depressive disorder, and cardiovascular epidemiology. The document references 18 of Lin's publications and provides an overview of his diverse experience and future research plans developing statistical tools and methods and applying them to biological problems.
A comprehensive study on disease risk predictions in machine learning IJECEIAES
Over recent years, multiple disease risk prediction models have been developed. These models use various patient characteristics to estimate the probability of outcomes over a certain period of time and hold the potential to improve decision making and individualize care. Discovering hidden patterns and interactions from medical databases with growing evaluation of the disease prediction model has become crucial. It needs many trials in traditional clinical findings that could complicate disease prediction. A Comprehensive study on different strategies used to predict disease is conferred in this paper. Applying these techniques to healthcare data, has improvement of risk prediction models to find out the patients who would get benefit from disease management programs to reduce hospital readmission and healthcare cost, but the results of these endeavors have been shifted.
IRJET- Cancer Disease Prediction using Machine Learning over Big DataIRJET Journal
1. The document discusses using machine learning algorithms to predict cancer and other diseases by analyzing big healthcare data. It specifically looks at using support vector machines (SVM) for cancer prediction and classification.
2. SVM is presented as a powerful machine learning tool for cancer classification and identification of biomarkers, drug targets, and cancer-driving genes. The paper also examines applying machine learning algorithms like SVM to predict outbreaks of chronic diseases in populations using medical data.
3. The researchers aim to improve disease prediction accuracy by addressing issues with incomplete or inconsistent medical data from different regions. They also seek to enable early diagnosis and treatment by analyzing large healthcare datasets with machine learning.
EFFICACY OF NON-NEGATIVE MATRIX FACTORIZATION FOR FEATURE SELECTION IN CANCER...IJDKP
Over the past few years, there has been a considerable spread of microarray technology in many biological patterns, particularly in those pertaining to cancer diseases like leukemia, prostate, colon cancer, etc. The primary bottleneck that one experiences in the proper understanding of such datasets lies in their dimensionality, and thus for an efficient and effective means of studying the same, a reduction in their dimension to a large extent is deemed necessary. This study is a bid to suggesting different algorithms and approaches for the reduction of dimensionality of such microarray datasets.This study exploits the matrix-like structure of such microarray data and uses a popular technique called Non-Negative Matrix Factorization (NMF) to reduce the dimensionality, primarily in the field of biological data. Classification accuracies are then compared for these algorithms.This technique gives an accuracy of 98%.
EFFICACY OF NON-NEGATIVE MATRIX FACTORIZATION FOR FEATURE SELECTION IN CANCER...IJDKP
Over the past few years, there has been a considerable spread of microarray technology in many
biological patterns, particularly in those pertaining to cancer diseases like leukemia, prostate, colon
cancer, etc. The primary bottleneck that one experiences in the proper understanding of such datasets lies
in their dimensionality, and thus for an efficient and effective means of studying the same, a reduction in
their dimension to a large extent is deemed necessary. This study is a bid to suggesting different algorithms
and approaches for the reduction of dimensionality of such microarray datasets.This study exploits the
matrix-like structure of such microarray data and uses a popular technique called Non-Negative Matrix
Factorization (NMF) to reduce the dimensionality, primarily in the field of biological data. Classification
accuracies are then compared for these algorithms.This technique gives an accuracy of 98%
ABSTRACT
Genome-wide transcription profiling is a powerful technique in studying disease susceptible footprints. Moreover, when applied to disease tissue it may reveal quantitative and qualitative alterations in gene expression that give information on the context or underlying basis for the disease and may provide a new diagnostic approach. However, the data obtained from high-density microarrays is highly complex and poses considerable challenges in data mining. Past researches prove that neuro diseases damage the brain network interaction, protein- protein interaction and gene-gene interaction. A number of neurological research paper also analyze the relationship among damaged part. Analysis of gene-gene interaction network drawn by using state-of-the-art gene database of Alzheimer’s patient can conclude a lot of information. In this paper we used gene dataset affected with Alzheimer’s disease and normal patient’s dataset from NCBI databank. After proper processing the .CEL affymetrix data using RMA, we use the processed data to find gene interaction outputs. Then we filter the output files using probe set filtering attributes p-value and fold count and draw a gene-gene interaction network. Then we analyze the interaction network using GeneMania software.
Performance enhancement of machine learning algorithm for breast cancer diagn...IJECEIAES
Breast cancer is the most fatal women’s cancer, and accurate diagnosis of this disease in the initial phase is crucial to abate death rates worldwide. The demand for computer-aided disease diagnosis technologies in healthcare is growing significantly to assist physicians in ensuring the effectual treatment of critical diseases. The vital purpose of this study is to analyze and evaluate the classification efficiency of several machine learning algorithms with hyperparameter optimization techniques using grid search and random search to reveal an efficient breast cancer diagnosis approach. Choosing the optimal combination of hyperparameters using hyperparameter optimization for machine learning models has a straight influence on the performance of models. According to the findings of several evaluation studies, the k-nearest neighbor is addressed in this study for effective diagnosis of breast cancer, which got a 100.00% recall value with hyperparameters found utilizing grid search. k-nearest neighbor, logistic regression, and multilayer perceptron obtained 99.42% accuracy after utilizing hyperparameter optimization. All machine learning models showed higher efficiency in breast cancer diagnosis with grid search-based hyperparameter optimization except for XGBoost. Therefore, the evaluation outcomes strongly validate the effectiveness and reliability of the proposed technique for breast cancer diagnosis.
BLOOD TUMOR PREDICTION USING DATA MINING TECHNIQUEShiij
Healthcare systems generate a huge data collected from medical tests. Data mining is the computing
process of discovering patterns in large data sets such as medical examinations. Blood diseases are not an
exception; there are many test data can be collected from their patients. In this paper, we applied data
mining techniques to discover the relations between blood test characteristics and blood tumor in order to
predict the disease in an early stage, which can be used to enhance the curing ability. We conducted
experiments in our blood test dataset using three different data mining techniques which are association
rules, rule induction and deep learning. The goal of our experiments is to generate models that can
distinguish patients with normal blood disease from patients who have blood tumor. We evaluated our
results using different metrics applied on real data collected from Gaza European hospital in Palestine.
The final results showed that association rules could give us the relationship between blood test
characteristics and blood tumor. Also, it demonstrated that deep learning classifiers has the best ability to
predict tumor types of blood diseases with an accuracy of 79.45%. Also, rule induction gave us an
explanation of rules that describes both tumor in blood and normal hematology.
BLOOD TUMOR PREDICTION USING DATA MINING TECHNIQUEShiij
Healthcare systems generate a huge data collected from medical tests. Data mining is the computing
process of discovering patterns in large data sets such as medical examinations. Blood diseases are not an
exception; there are many test data can be collected from their patients. In this paper, we applied data
mining techniques to discover the relations between blood test characteristics and blood tumor in order to
predict the disease in an early stage, which can be used to enhance the curing ability. We conducted
experiments in our blood test dataset using three different data mining techniques which are association
rules, rule induction and deep learning. The goal of our experiments is to generate models that can
distinguish patients with normal blood disease from patients who have blood tumor. We evaluated our
results using different metrics applied on real data collected from Gaza European hospital in Palestine.
The final results showed that association rules could give us the relationship between blood test
characteristics and blood tumor. Also, it demonstrated that deep learning classifiers has the best ability to
predict tumor types of blood diseases with an accuracy of 79.45%. Also, rule induction gave us an
explanation of rules that describes both tumor in blood and normal hematology.
BLOOD TUMOR PREDICTION USING DATA MINING TECHNIQUEShiij
Healthcare systems generate a huge data collected from medical tests. Data mining is the computing process of discovering patterns in large data sets such as medical examinations. Blood diseases are not an exception; there are many test data can be collected from their patients. In this paper, we applied data mining techniques to discover the relations between blood test characteristics and blood tumor in order to predict the disease in an early stage, which can be used to enhance the curing ability. We conducted experiments in our blood test dataset using three different data mining techniques which are association rules, rule induction and deep learning. The goal of our experiments is to generate models that can distinguish patients with normal blood disease from patients who have blood tumor. We evaluated our
results using different metrics applied on real data collected from Gaza European hospital in Palestine. The final results showed that association rules could give us the relationship between blood test characteristics and blood tumor. Also, it demonstrated that deep learning classifiers has the best ability to predict tumor types of blood diseases with an accuracy of 79.45%. Also, rule induction gave us an explanation of rules that describes both tumor in blood and normal hematology.
1) AI systems like Adam and Eve have discovered new scientific knowledge by autonomously generating and testing hypotheses about yeast genes using public databases and laboratory experiments.
2) AI is being applied throughout the drug development process, including target identification, compound design and synthesis, clinical trial optimization, and drug repurposing.
3) Partnerships between pharmaceutical companies and AI firms are exploring applications like generating new immuno-oncology treatments, metabolic disease therapies, and cancer treatments through large-scale data analysis.
Classification AlgorithmBased Analysis of Breast Cancer DataIIRindia
The classification algorithms are very frequently used algorithms for analyzing various kinds of data available in different repositories which have real world applications. The main objective of this research work is to find the performance of classification algorithms in analyzing Breast Cancer data via analyzing the mammogram images based its characteristics.Different attribute values of cancer affected mammogram images are considered for analysis in this work. The Patients food habits, age of the patients, their life styles, occupation, their problem about the diseases and other information are taken into account for classification. Finally, performance of classification algorithms J48, CART and ADTree are given with its accuracy. The accuracy of taken algorithms is measured by various measures like specificity, sensitivity and kappa statistics (Errors).
Patterns discovered from based on collected molecular profiles of patient tumour samples, and also clinical metadata, could be used to provide personalized cancer treatment to patients with
similar molecular subtypes. Computational algorithms for cancer diagnosis, prognosis, and therapeutics that can recognize specific functions and aid in classifiers based on a plethora of
publicly accessible cancer research outcomes are needed. Machine learning, a branch of artificial intelligence, has a great deal of potential for problem solving in cryptic cancer
datasets, as per a literature study. We focus on the new state of machine learning applications in cancer research in this study, illustrating trends and analysing major accomplishments,
roadblocks, and challenges along the way to clinic implementation. In the context of noninvasive treating cancer using diet-based and natural biomarkers, we propose a novel machine learning algorithm.
IRJET - E-Health Chain and Anticipation of Future DiseaseIRJET Journal
The document proposes an E-Health Chain system that uses machine learning algorithms to predict future diseases and maintain electronic health records more efficiently than traditional paper-based systems. Key features include digital prescriptions that allow patients to purchase medicines using a unique ID, electronic health records that give doctors access to patient data from remote locations, emergency response features like ambulance dispatch, and medication reminders for patients. The system aims to help patients take preventive measures by predicting future diseases using algorithms trained on historical health data. A literature review covers previous research on disease prediction using decision trees, neural networks, and other machine learning methods applied to medical datasets.
USING DATA MINING TECHNIQUES FOR DIAGNOSIS AND PROGNOSIS OF CANCER DISEASEIJCSEIT Journal
Breast cancer is one of the leading cancers for women in developed countries including India. It is the
second most common cause of cancer death in women. The high incidence of breast cancer in women has
increased significantly in the last years. In this paper we have discussed various data mining approaches
that have been utilized for breast cancer diagnosis and prognosis. Breast Cancer Diagnosis is
distinguishing of benign from malignant breast lumps and Breast Cancer Prognosis predicts when Breast
Cancer is to recur in patients that have had their cancers excised. This study paper summarizes various
review and technical articles on breast cancer diagnosis and prognosis also we focus on current research
being carried out using the data mining techniques to enhance the breast cancer diagnosis and prognosis.
Mining of Important Informative Genes and Classifier Construction for Cancer ...ijsc
Microarray is a useful technique for measuring expression data of thousands or more of genes simultaneously. One of challenges in classification of cancer using high-dimensional gene expression data is to select a minimal number of relevant genes which can maximize classification accuracy. Because of the distinct characteristics inherent to specific cancerous gene expression profiles, developing flexible and robust gene identification methods is extremely fundamental. Many gene selection methods as well as their corresponding classifiers have been proposed. In the proposed method, a single gene with high classdiscrimination capability is selected and classification rules are generated for cancer based on gene expression profiles. The method first computes importance factor of each gene of experimental cancer dataset by counting number of linguistic terms (defined in terms of different discreet quantity) with high class discrimination capability according to their depended degree of classes. Then initial important genes are selected according to high importance factor of each gene and form initial reduct. Then traditional kmeans clustering algorithm is applied on each selected gene of initial reduct and compute missclassification errors of individual genes. The final reduct is formed by selecting most important genes with respect to less miss-classification errors. Then a classifier is constructed based on decision rules induced by selected important genes (single) from training dataset to classify cancerous and non-cancerous samples of experimental test dataset. The proposed method test on four publicly available cancerous gene expression test dataset. In most of cases, accurate classifications outcomes are obtained by just using important (single) genes that are highly correlated with the pathogenesis cancer are identified. Also to prove the robustness of proposed method compares the outcomes (correctly classified instances) with some existing well known classifiers.
Similar to A Survey on Various Disease Prediction Techniques (20)
‘Six Sigma Technique’ A Journey Through its Implementationijtsrd
The manufacturing industries all over the world are facing tough challenges for growth, development and sustainability in today’s competitive environment. They have to achieve apex position by adapting with the global competitive environment by delivering goods and services at low cost, prime quality and better price to increase wealth and consumer satisfaction. Cost Management ensures profit, growth and sustainability of the business with implementation of Continuous Improvement Technique like Six Sigma. This leads to optimize Business performance. The method drives for customer satisfaction, low variation, reduction in waste and cycle time resulting into a competitive advantage over other industries which did not implement it. The main objective of this paper ‘Six Sigma Technique A Journey Through Its Implementation’ is to conceptualize the effectiveness of Six Sigma Technique through the journey of its implementation. Aditi Sunilkumar Ghosalkar "‘Six Sigma Technique’: A Journey Through its Implementation" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64546.pdf Paper Url: https://www.ijtsrd.com/other-scientific-research-area/other/64546/‘six-sigma-technique’-a-journey-through-its-implementation/aditi-sunilkumar-ghosalkar
Edge Computing in Space Enhancing Data Processing and Communication for Space...ijtsrd
Edge computing, a paradigm that involves processing data closer to its source, has gained significant attention for its potential to revolutionize data processing and communication in space missions. With the increasing complexity and data volume generated by modern space missions, traditional centralized computing approaches face challenges related to latency, bandwidth, and security. Edge computing in space, involving on board processing and analysis of data, offers promising solutions to these challenges. This paper explores the concept of edge computing in space, its benefits, applications, and future prospects in enhancing space missions. Manish Verma "Edge Computing in Space: Enhancing Data Processing and Communication for Space Missions" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64541.pdf Paper Url: https://www.ijtsrd.com/computer-science/artificial-intelligence/64541/edge-computing-in-space-enhancing-data-processing-and-communication-for-space-missions/manish-verma
Dynamics of Communal Politics in 21st Century India Challenges and Prospectsijtsrd
Communal politics in India has evolved through centuries, weaving a complex tapestry shaped by historical legacies, colonial influences, and contemporary socio political transformations. This research comprehensively examines the dynamics of communal politics in 21st century India, emphasizing its historical roots, socio political dynamics, economic implications, challenges, and prospects for mitigation. The historical perspective unravels the intricate interplay of religious identities and power dynamics from ancient civilizations to the impact of colonial rule, providing insights into the evolution of communalism. The socio political dynamics section delves into the contemporary manifestations, exploring the roles of identity politics, socio economic disparities, and globalization. The economic implications section highlights how communal politics intersects with economic issues, perpetuating disparities and influencing resource allocation. Challenges posed by communal politics are scrutinized, revealing multifaceted issues ranging from social fragmentation to threats against democratic values. The prospects for mitigation present a multifaceted approach, incorporating policy interventions, community engagement, and educational initiatives. The paper conducts a comparative analysis with international examples, identifying common patterns such as identity politics and economic disparities. It also examines unique challenges, emphasizing Indias diverse religious landscape, historical legacy, and secular framework. Lessons for effective strategies are drawn from international experiences, offering insights into inclusive policies, interfaith dialogue, media regulation, and global cooperation. By scrutinizing historical epochs, contemporary dynamics, economic implications, and international comparisons, this research provides a comprehensive understanding of communal politics in India. The proposed strategies for mitigation underscore the importance of a holistic approach to foster social harmony, inclusivity, and democratic values. Rose Hossain "Dynamics of Communal Politics in 21st Century India: Challenges and Prospects" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64528.pdf Paper Url: https://www.ijtsrd.com/humanities-and-the-arts/history/64528/dynamics-of-communal-politics-in-21st-century-india-challenges-and-prospects/rose-hossain
Assess Perspective and Knowledge of Healthcare Providers Towards Elehealth in...ijtsrd
Background and Objective Telehealth has become a well known tool for the delivery of health care in Saudi Arabia, and the perspective and knowledge of healthcare providers are influential in the implementation, adoption and advancement of the method. This systematic review was conducted to examine the current literature base regarding telehealth and the related healthcare professional perspective and knowledge in the Kingdom of Saudi Arabia. Materials and Methods This systematic review was conducted by searching 7 databases including, MEDLINE, CINHAL, Web of Science, Scopus, PubMed, PsycINFO, and ProQuest Central. Studies on healthcare practitioners telehealth knowledge and perspectives published in English in Saudi Arabia from 2000 to 2023 were included. Boland directed this comprehensive review. The researchers examined each connected study using the AXIS tool, which evaluates cross sectional systematic reviews. Narrative synthesis was used to summarise and convey the data. Results Out of 1840 search results, 10 studies were included. Positive outlook and limited knowledge among providers were seen across trials. Healthcare professionals like telehealth for its ability to improve quality, access, and delivery, save time and money, and be successful. Age, gender, occupation, and work experience also affect health workers knowledge. In Saudi Arabia, healthcare professionals face inadequate expert assistance, patient privacy, internet connection concerns, lack of training courses, lack of telehealth understanding, and high costs while performing telemedicine. Conclusions Healthcare practitioners telehealth perceptions and knowledge were examined in this systematic study. Its collection of concerned experts different personal attitudes and expertise would help enhance telehealths implementation in Saudi Arabia, develop its healthcare delivery alternative, and eliminate frequent problems. Badriah Mousa I Mulayhi | Dr. Jomin George | Judy Jenkins "Assess Perspective and Knowledge of Healthcare Providers Towards Elehealth in Saudi Arabia: A Systematic Review" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64535.pdf Paper Url: https://www.ijtsrd.com/medicine/other/64535/assess-perspective-and-knowledge-of-healthcare-providers-towards-elehealth-in-saudi-arabia-a-systematic-review/badriah-mousa-i-mulayhi
The Impact of Digital Media on the Decentralization of Power and the Erosion ...ijtsrd
The impact of digital media on the distribution of power and the weakening of traditional gatekeepers has gained considerable attention in recent years. The adoption of digital technologies and the internet has resulted in declining influence and power for traditional gatekeepers such as publishing houses and news organizations. Simultaneously, digital media has facilitated the emergence of new voices and players in the media industry. Digital medias impact on power decentralization and gatekeeper erosion is visible in several ways. One significant aspect is the democratization of information, which enables anyone with an internet connection to publish and share content globally, leading to citizen journalism and bypassing traditional gatekeepers. Another aspect is the disruption of conventional media industry business models, as traditional organizations struggle to adjust to the decrease in advertising revenue and the rise of digital platforms. Alternative business models, such as subscription models and crowdfunding, have become more prevalent, leading to the emergence of new players. Overall, the impact of digital media on the distribution of power and the weakening of traditional gatekeepers has brought about significant changes in the media landscape and the way information is shared. Further research is required to fully comprehend the implications of these changes and their impact on society. Dr. Kusum Lata "The Impact of Digital Media on the Decentralization of Power and the Erosion of Traditional Gatekeepers" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64544.pdf Paper Url: https://www.ijtsrd.com/humanities-and-the-arts/political-science/64544/the-impact-of-digital-media-on-the-decentralization-of-power-and-the-erosion-of-traditional-gatekeepers/dr-kusum-lata
Online Voices, Offline Impact Ambedkars Ideals and Socio Political Inclusion ...ijtsrd
This research investigates the nexus between online discussions on Dr. B.R. Ambedkars ideals and their impact on social inclusion among college students in Gurugram, Haryana. Surveying 240 students from 12 government colleges, findings indicate that 65 actively engage in online discussions, with 80 demonstrating moderate to high awareness of Ambedkars ideals. Statistically significant correlations reveal that higher online engagement correlates with increased awareness p 0.05 and perceived social inclusion. Variations across colleges and a notable effect of college type on perceived social inclusion highlight the influence of contextual factors. Furthermore, the intersectional analysis underscores nuanced differences based on gender, caste, and socio economic status. Dr. Kusum Lata "Online Voices, Offline Impact: Ambedkar's Ideals and Socio-Political Inclusion - A Study of Gurugram District" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64543.pdf Paper Url: https://www.ijtsrd.com/humanities-and-the-arts/political-science/64543/online-voices-offline-impact-ambedkars-ideals-and-sociopolitical-inclusion--a-study-of-gurugram-district/dr-kusum-lata
Problems and Challenges of Agro Entreprenurship A Studyijtsrd
Noting calls for contextualizing Agro entrepreneurs problems and challenges of the agro entrepreneurs and for greater attention to the Role of entrepreneurs in agro entrepreneurship research, we conduct a systematic literature review of extent research in agriculture entrepreneurship to overcome the study objectives of complications of agro entrepreneurs through various factors, Development of agriculture products is a key factor for the overall economic growth of agro entrepreneurs Agro Entrepreneurs produces firsthand large scale employment, utilizes the labor and natural resources, This research outlines the problems of Weather and Soil Erosions, Market price fluctuation, stimulates labor cost problems, reduces concentration of Price volatility, Dependency on Intermediaries, induces Limited Bargaining Power, and Storage and Transportation Costs. This paper mainly devoted to highlight Problems and challenges faced for the sustainable of Agro Entrepreneurs in India. Vinay Prasad B "Problems and Challenges of Agro Entreprenurship - A Study" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64540.pdf Paper Url: https://www.ijtsrd.com/other-scientific-research-area/other/64540/problems-and-challenges-of-agro-entreprenurship--a-study/vinay-prasad-b
Comparative Analysis of Total Corporate Disclosure of Selected IT Companies o...ijtsrd
Disclosure is a process through which a business enterprise communicates with external parties. A corporate disclosure is communication of financial and non financial information of the activities of a business enterprise to the interested entities. Corporate disclosure is done through publishing annual reports. So corporate disclosure through annual reports plays a vital role in the life of all the companies and provides valuable information to investors. The basic objectives of corporate disclosure is to give a true and fair view of companies to the parties related either directly or indirectly like owner, government, creditors, shareholders etc. in the companies act, provisions have been made about mandatory and voluntary disclosure. The IT sector in India is rapidly growing, the trend to invest in the IT sector is rising and employment opportunities in IT sectors are also increasing. Therefore the IT sector is expected to have fair, full and adequate disclosure of all information. Unfair and incomplete disclosure may adversely affect the entire economy. A research study on disclosure practices of IT companies could play an important role in this regard. Hence, the present research study has been done to study and review comparative analysis of total corporate disclosure of selected IT companies of India and to put forward overall findings and suggestions with a view to increase disclosure score of these companies. The researcher hopes that the present research study will be helpful to all selected Companies for improving level of corporate disclosure through annual reports as well as the government, creditors, investors, all business organizations and upcoming researcher for comparative analyses of level of corporate disclosure with special reference to selected IT companies. Dr. Vaibhavi D. Thaker "Comparative Analysis of Total Corporate Disclosure of Selected IT Companies of India" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64539.pdf Paper Url: https://www.ijtsrd.com/other-scientific-research-area/other/64539/comparative-analysis-of-total-corporate-disclosure-of-selected-it-companies-of-india/dr-vaibhavi-d-thaker
The Impact of Educational Background and Professional Training on Human Right...ijtsrd
This study investigated the impact of educational background and professional training on human rights awareness among secondary school teachers in the Marathwada region of Maharashtra, India. The key findings reveal that higher levels of education, particularly a master’s degree, and fields of study related to education, humanities, or social sciences are associated with greater human rights awareness among teachers. Additionally, both pre service teacher training and in service professional development programs focused on human rights education significantly enhance teacher’s knowledge, skills, and competencies in promoting human rights principles in their classrooms. Baig Ameer Bee Mirza Abdul Aziz | Dr. Syed Azaz Ali Amjad Ali "The Impact of Educational Background and Professional Training on Human Rights Awareness among Secondary School Teachers" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64529.pdf Paper Url: https://www.ijtsrd.com/humanities-and-the-arts/education/64529/the-impact-of-educational-background-and-professional-training-on-human-rights-awareness-among-secondary-school-teachers/baig-ameer-bee-mirza-abdul-aziz
A Study on the Effective Teaching Learning Process in English Curriculum at t...ijtsrd
“One Language sets you in a corridor for life. Two languages open every door along the way” Frank Smith English as a foreign language or as a second language has been ruling in India since the period of Lord Macaulay. But the question is how much we teach or learn English properly in our culture. Is there any scope to use English as a language rather than a subject How much we learn or teach English without any interference of mother language specially in the classroom teaching learning scenario in West Bengal By considering all these issues the researcher has attempted in this article to focus on the effective teaching learning process comparing to other traditional strategies in the field of English curriculum at the secondary level to investigate whether they fulfill the present teaching learning requirements or not by examining the validity of the present curriculum of English. The purpose of this study is to focus on the effectiveness of the systematic, scientific, sequential and logical transaction of the course between the teachers and the learners in the perspective of the 5Es programme that is engage, explore, explain, extend and evaluate. Sanchali Mondal | Santinath Sarkar "A Study on the Effective Teaching Learning Process in English Curriculum at the Secondary Level of West Bengal" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd62412.pdf Paper Url: https://www.ijtsrd.com/humanities-and-the-arts/education/62412/a-study-on-the-effective-teaching-learning-process-in-english-curriculum-at-the-secondary-level-of-west-bengal/sanchali-mondal
The Role of Mentoring and Its Influence on the Effectiveness of the Teaching ...ijtsrd
This paper reports on a study which was conducted to investigate the role of mentoring and its influence on the effectiveness of the teaching of Physics in secondary schools in the South West Region of Cameroon. The study adopted the convergent parallel mixed methods design, focusing on respondents in secondary schools in the South West Region of Cameroon. Both quantitative and qualitative data were collected, analysed separately, and the results were compared to see if the findings confirm or disconfirm each other. The quantitative analysis found that majority of the respondents 72 of Physics teachers affirmed that they had more experienced colleagues as mentors to help build their confidence, improve their teaching, and help them improve their effectiveness and efficiency in guiding learners’ achievements. Only 28 of the respondents disagreed with these statements. With majority respondents 72 agreeing with the statements, it implies that in most secondary schools, experienced Physics teachers act as mentors to build teachers’ confidence in teaching and improving students’ learning. The interview qualitative data analysis summarized how secondary school Principals use meetings with mentors and mentees to promote mentorship in the school milieu. This has helped strengthen teachers’ classroom practices in secondary schools in the South West Region of Cameroon. With the results confirming each other, the study recommends that mentoring should focus on helping teachers employ social interactions and instructional practices feedback and clarity in teaching that have direct measurable impact on students’ learning achievements. Andrew Ngeim Sumba | Frederick Ebot Ashu | Peter Agborbechem Tambi "The Role of Mentoring and Its Influence on the Effectiveness of the Teaching of Physics in Secondary Schools in the South West Region of Cameroon" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64524.pdf Paper Url: https://www.ijtsrd.com/management/management-development/64524/the-role-of-mentoring-and-its-influence-on-the-effectiveness-of-the-teaching-of-physics-in-secondary-schools-in-the-south-west-region-of-cameroon/andrew-ngeim-sumba
Design Simulation and Hardware Construction of an Arduino Microcontroller Bas...ijtsrd
This study primarily focuses on the design of a high side buck converter using an Arduino microcontroller. The converter is specifically intended for use in DC DC applications, particularly in standalone solar PV systems where the PV output voltage exceeds the load or battery voltage. To evaluate the performance of the converter, simulation experiments are conducted using Proteus Software. These simulations provide insights into the input and output voltages, currents, powers, and efficiency under different state of charge SoC conditions of a 12V,70Ah rechargeable lead acid battery. Additionally, the hardware design of the converter is implemented, and practical data is collected through operation, monitoring, and recording. By comparing the simulation results with the practical results, the efficiency and performance of the designed converter are assessed. The findings indicate that while the buck converter is suitable for practical use in standalone PV systems, its efficiency is compromised due to a lower output current. Chan Myae Aung | Dr. Ei Mon "Design Simulation and Hardware Construction of an Arduino-Microcontroller Based DC-DC High-Side Buck Converter for Standalone PV System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64518.pdf Paper Url: https://www.ijtsrd.com/engineering/mechanical-engineering/64518/design-simulation-and-hardware-construction-of-an-arduinomicrocontroller-based-dcdc-highside-buck-converter-for-standalone-pv-system/chan-myae-aung
Sustainable Energy by Paul A. Adekunte | Matthew N. O. Sadiku | Janet O. Sadikuijtsrd
Energy becomes sustainable if it meets the needs of the present without compromising the ability of future generations to meet their own needs. Some of the definitions of sustainable energy include the considerations of environmental aspects such as greenhouse gas emissions, social, and economic aspects such as energy poverty. Generally far more sustainable than fossil fuel are renewable energy sources such as wind, hydroelectric power, solar, and geothermal energy sources. Worthy of note is that some renewable energy projects, like the clearing of forests to produce biofuels, can cause severe environmental damage. The sustainability of nuclear power which is a low carbon source is highly debated because of concerns about radioactive waste, nuclear proliferation, and accidents. The switching from coal to natural gas has environmental benefits, including a lower climate impact, but could lead to delay in switching to more sustainable options. “Carbon capture and storage” can be built into power plants to remove the carbon dioxide CO2 emissions, but this technology is expensive and has rarely been implemented. Leading non renewable energy sources around the world is fossil fuels, coal, petroleum, and natural gas. Nuclear energy is usually considered another non renewable energy source, although nuclear energy itself is a renewable energy source, but the material used in nuclear power plants is not. The paper addresses the issue of sustainable energy, its attendant benefits to the future generation, and humanity in general. Paul A. Adekunte | Matthew N. O. Sadiku | Janet O. Sadiku "Sustainable Energy" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64534.pdf Paper Url: https://www.ijtsrd.com/engineering/electrical-engineering/64534/sustainable-energy/paul-a-adekunte
Concepts for Sudan Survey Act Implementations Executive Regulations and Stand...ijtsrd
This paper aims to outline the executive regulations, survey standards, and specifications required for the implementation of the Sudan Survey Act, and for regulating and organizing all surveying work activities in Sudan. The act has been discussed for more than 5 years. The Land Survey Act was initiated by the Sudan Survey Authority and all official legislations were headed by the Sudan Ministry of Justice till it was issued in 2022. The paper presents conceptual guidelines to be used for the Survey Act implementation and to regulate the survey work practice, standardizing the field surveys, processing, quality control, procedures, and the processes related to survey work carried out by the stakeholders and relevant authorities in Sudan. The conceptual guidelines are meant to improve the quality and harmonization of geospatial data and to aid decision making processes as well as geospatial information systems. The established comprehensive executive regulations will govern and regulate the implementation of the Sudan Survey Geomatics Act in all surveying and mapping practices undertaken by the Sudan Survey Authority SSA and state local survey departments for public or private sector organizations. The targeted standards and specifications include the reference frame, projection, coordinate systems, and the guidelines and specifications that must be followed in the field of survey work, processes, and mapping products. In the last few decades, there has been a growing awareness of the importance of geomatics activities and measurements on the Earths surface in space and time, together with observing and mapping the changes. In such cases, data must be captured promptly, standardized, and obtained with more accuracy and specified in much detail. The paper will also highlight the current situation in Sudan, the degree to which survey standards are used, the problems encountered, and the errors that arise from not using the standards and survey specifications. Kamal A. A. Sami "Concepts for Sudan Survey Act Implementations - Executive Regulations and Standards" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd63484.pdf Paper Url: https://www.ijtsrd.com/engineering/civil-engineering/63484/concepts-for-sudan-survey-act-implementations--executive-regulations-and-standards/kamal-a-a-sami
Towards the Implementation of the Sudan Interpolated Geoid Model Khartoum Sta...ijtsrd
The discussions between ellipsoid and geoid have invoked many researchers during the recent decades, especially during the GNSS technology era, which had witnessed a great deal of development but still geoid undulation requires more investigations. To figure out a solution for Sudans local geoid, this research has tried to intake the possibility of determining the geoid model by following two approaches, gravimetric and geometrical geoid model determination, by making use of GNSS leveling benchmarks at Khartoum state. The Benchmarks are well distributed in the study area, in which, the horizontal coordinates and the height above the ellipsoid have been observed by GNSS while orthometric heights were carried out using precise leveling. The Global Geopotential Model GGM represented in EGM2008 has been exploited to figure out the geoid undulation at the benchmarks in the study area. This is followed by a fitting process, that has been done to suit the geoid undulation data which has been computed using GNSS leveling data and geoid undulation inspired by the EGM2008. Two geoid surfaces were created after the fitting process to ensure that they are identical and both of them could be counted for getting the same geoid undulation with an acceptable accuracy. In this respect, statistical operation played an important role in ensuring the consistency and integrity of the model by applying cross validation techniques splitting the data into training and testing datasets for building the geoid model and testing its eligibility. The geometrical solution for geoid undulation computation has been utilized by applying straightforward equations that facilitate the calculation of the geoid undulation directly through applying statistical techniques for the GNSS leveling data of the study area to get the common equation parameters values that could be utilized to calculate geoid undulation of any position in the study area within the claimed accuracy. Both systems were checked and proved eligible to be used within the study area with acceptable accuracy which may contribute to solving the geoid undulation problem in the Khartoum area, and be further generalized to determine the geoid model over the entire country, and this could be considered in the future, for regional and continental geoid model. Ahmed M. A. Mohammed. | Kamal A. A. Sami "Towards the Implementation of the Sudan Interpolated Geoid Model (Khartoum State Case Study)" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd63483.pdf Paper Url: https://www.ijtsrd.com/engineering/civil-engineering/63483/towards-the-implementation-of-the-sudan-interpolated-geoid-model-khartoum-state-case-study/ahmed-m-a-mohammed
Activating Geospatial Information for Sudans Sustainable Investment Mapijtsrd
Sudan is witnessing an acceleration in the processes of development and transformation in the performance of government institutions to raise the productivity and investment efficiency of the government sector. The development plans and investment opportunities have focused on achieving national goals in various sectors. This paper aims to illuminate the path to the future and provide geospatial data and information to develop the investment climate and environment for all sized businesses, and to bridge the development gap between the Sudan states. The Sudan Survey Authority SSA is the main advisor to the Sudan Government in conducting surveying, mappings, designing, and developing systems related to geospatial data and information. In recent years, SSA made a strategic partnership with the Ministry of Investment to activate Geospatial Information for Sudans Sustainable Investment and in particular, for the preparation and implementation of the Sudan investment map, based on the directives and objectives of the Ministry of Investment MI in Sudan. This paper comes within the framework of activating the efforts of the Ministry of Investment to develop technical investment services by applying techniques adopted by the Ministry and its strategic partners for advancing investment processes in the country. Kamal A. A. Sami "Activating Geospatial Information for Sudan's Sustainable Investment Map" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd63482.pdf Paper Url: https://www.ijtsrd.com/engineering/information-technology/63482/activating-geospatial-information-for-sudans-sustainable-investment-map/kamal-a-a-sami
Educational Unity Embracing Diversity for a Stronger Societyijtsrd
In a rapidly changing global landscape, the importance of education as a unifying force cannot be overstated. This paper explores the crucial role of educational unity in fostering a stronger and more inclusive society through the embrace of diversity. By examining the benefits of diverse learning environments, the paper aims to highlight the positive impact on societal strength. The discussion encompasses various dimensions, from curriculum design to classroom dynamics, and emphasizes the need for educational institutions to become catalysts for unity in diversity. It highlights the need for a paradigm shift in educational policies, curricula, and pedagogical approaches to ensure that they are reflective of the diverse fabric of society. This paper also addresses the challenges associated with implementing inclusive educational practices and offers practical strategies for overcoming barriers. It advocates for collaborative efforts between educational institutions, policymakers, and communities to create a supportive ecosystem that promotes diversity and unity. Mr. Amit Adhikari | Madhumita Teli | Gopal Adhikari "Educational Unity: Embracing Diversity for a Stronger Society" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64525.pdf Paper Url: https://www.ijtsrd.com/humanities-and-the-arts/education/64525/educational-unity-embracing-diversity-for-a-stronger-society/mr-amit-adhikari
Integration of Indian Indigenous Knowledge System in Management Prospects and...ijtsrd
The diversity of indigenous knowledge systems in India is vast and can vary significantly between different communities and regions. Preserving and respecting these knowledge systems is crucial for maintaining cultural heritage, promoting sustainable practices, and fostering cross cultural understanding. In this paper, an overview of the prospects and challenges associated with incorporating Indian indigenous knowledge into management is explored. It is found that IIKS helps in management in many areas like sustainable development, tourism, food security, natural resource management, cultural preservation and innovation, etc. However, IIKS integration with management faces some challenges in the form of a lack of documentation, cultural sensitivity, language barriers legal framework, etc. Savita Lathwal "Integration of Indian Indigenous Knowledge System in Management: Prospects and Challenges" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd63500.pdf Paper Url: https://www.ijtsrd.com/management/accounting-and-finance/63500/integration-of-indian-indigenous-knowledge-system-in-management-prospects-and-challenges/savita-lathwal
DeepMask Transforming Face Mask Identification for Better Pandemic Control in...ijtsrd
The COVID 19 pandemic has highlighted the crucial need of preventive measures, with widespread use of face masks being a key method for slowing the viruss spread. This research investigates face mask identification using deep learning as a technological solution to be reducing the risk of coronavirus transmission. The proposed method uses state of the art convolutional neural networks CNNs and transfer learning to automatically recognize persons who are not wearing masks in a variety of circumstances. We discuss how this strategy improves public health and safety by providing an efficient manner of enforcing mask wearing standards. The report also discusses the obstacles, ethical concerns, and prospective applications of face mask detection systems in the ongoing fight against the pandemic. Dilip Kumar Sharma | Aaditya Yadav "DeepMask: Transforming Face Mask Identification for Better Pandemic Control in the COVID-19 Era" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64522.pdf Paper Url: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/64522/deepmask-transforming-face-mask-identification-for-better-pandemic-control-in-the-covid19-era/dilip-kumar-sharma
Streamlining Data Collection eCRF Design and Machine Learningijtsrd
Efficient and accurate data collection is paramount in clinical trials, and the design of Electronic Case Report Forms eCRFs plays a pivotal role in streamlining this process. This paper explores the integration of machine learning techniques in the design and implementation of eCRFs to enhance data collection efficiency. We delve into the synergies between eCRF design principles and machine learning algorithms, aiming to optimize data quality, reduce errors, and expedite the overall data collection process. The application of machine learning in eCRF design brings forth innovative approaches to data validation, anomaly detection, and real time adaptability. This paper discusses the benefits, challenges, and future prospects of leveraging machine learning in eCRF design for streamlined and advanced data collection in clinical trials. Dhanalakshmi D | Vijaya Lakshmi Kannareddy "Streamlining Data Collection: eCRF Design and Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd63515.pdf Paper Url: https://www.ijtsrd.com/biological-science/biotechnology/63515/streamlining-data-collection-ecrf-design-and-machine-learning/dhanalakshmi-d
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1. International Journal of Trend in
International Open Access Journal
ISSN No: 2456
@ IJTSRD | Available Online @ www.ijtsrd.com
A Survey on Various Disease Prediction Techniques
C. Leancy Jannet
Department of CT, Sri Krishna College
ABSTRACT
An analysis of various diseases have been predicted
using multiple data mining and text mining
techniques. In this article we are going to discuss
about 6 prediction techniques. Using gene expression
pattern we predict the disease outcome and
implementation of pathway based approach for
classifying disease based on hyper box principles, we
also present a novel hybrid prediction model with
missing value imputation (HPM-MI) which analyze
imputation using simple k-means clustering. A
technique based on CCAR (Constraint Clas
Association Rule) has been used for reducing time
consumption in prediction of a particular disease. We
have discussed about text mining technique and their
applications. Another technique has also been studied
about hyper triglyceride mia from anthropom
measures which diverge according to age and gender.
Using multilayer classifiers for disease prediction we
can achieve high diagnosis accuracy and high
performance.
Keyword: Prediction, Genes, Data Mining, Text
Mining, Hyper triglyceride mi a, Missing Values, Hmv
and Classifiers
1. INTRODUCTION
In this article we are going to discuss about 6
techniques for predicting disease. In the first
technique diseases can be predicted by gene
expression pattern. For selective distinctive genes
feature selection algorithm is enrolled. For
classification 2 approaches are used network based
approaches and pathway based approach and through
hyper box representation diseases are classified
The second technique is predicting the disease using
MVI we first evaluate 11 missing data imputation
techniques practically and then find the perfect
method for grasping missing values from dataset
using k-means clustering[2]. The third prediction
International Journal of Trend in Scientific Research and Development (IJTSRD)
International Open Access Journal | www.ijtsrd.com
ISSN No: 2456 - 6470 | Volume - 2 | Issue – 6 | Sep
www.ijtsrd.com | Volume – 2 | Issue – 6 | Sep-Oct 2018
n Various Disease Prediction Techniques
Leancy Jannet1
, G. V. Sumalatha2
1
Student, 2
Assistant Professor
Sri Krishna College of Arts and Science, Coimbatore
various diseases have been predicted
using multiple data mining and text mining
techniques. In this article we are going to discuss
about 6 prediction techniques. Using gene expression
pattern we predict the disease outcome and
ased approach for
box principles, we
also present a novel hybrid prediction model with
MI) which analyze
means clustering. A
(Constraint Class
Association Rule) has been used for reducing time
consumption in prediction of a particular disease. We
have discussed about text mining technique and their
applications. Another technique has also been studied
from anthropometric
measures which diverge according to age and gender.
Using multilayer classifiers for disease prediction we
can achieve high diagnosis accuracy and high
Prediction, Genes, Data Mining, Text
Missing Values, Hmv
In this article we are going to discuss about 6
techniques for predicting disease. In the first
technique diseases can be predicted by gene
expression pattern. For selective distinctive genes
selection algorithm is enrolled. For
classification 2 approaches are used network based
approaches and pathway based approach and through
hyper box representation diseases are classified [1].
The second technique is predicting the disease using
st evaluate 11 missing data imputation
techniques practically and then find the perfect
method for grasping missing values from dataset
means clustering[2]. The third prediction
technique is mining CARs (class association rules)
which locates relationship among item
labels. These item set constraints minimize CARs and
lessen the search space and enhance the performance,
first a tree structure is initiated for efficient mining,
second 2 theorems for soon trimming rare item
lastly efficient and fast algorithm for mining CARs 2
approaches are used pre
processing [3]. The fourth technique is text mining
which help researchers in assessing scientific
literature. Information can be
occurences based method and NLP
and text mining tools are discussed
technique is hyper triglyceride
measures based on data mining. Many diseases can be
predicted by the change in hyper
varies according to age and gender
technique is multilayer classifier for disease
prediction. A huge number of prediction models can
be build from data mining techniques. Here the
concept of machine learning is extended
learning are additionally classified into supervised and
unsupervised learning.
2. RELATED WORK
2.1. Pathway Based Approach
Through gene expression diseases can be predicted
where samples of genes are sketched as specimen of
different disease states for understanding the disease
phenotype. The computation of gene expression
estimate disease outcome. By using feature selection
algorithm we can pick a batch of genes which are
differently expressed. In network based approaches
disease module based methods suspect that all cellular
element that are owned to the identical topological,
working or disease module have a inflated chance of
having same disease. Greedy search is executed over
a PIN (People of Information Network) to pinpoint a
number of gene modules whose mean countenance is
Research and Development (IJTSRD)
www.ijtsrd.com
6 | Sep – Oct 2018
Oct 2018 Page: 734
n Various Disease Prediction Techniques
Coimbatore
(class association rules)
ationship among item sets and class
labels. These item set constraints minimize CARs and
lessen the search space and enhance the performance,
first a tree structure is initiated for efficient mining,
second 2 theorems for soon trimming rare item set,
y efficient and fast algorithm for mining CARs 2
processing and post
[3]. The fourth technique is text mining
which help researchers in assessing scientific
can be withdrawn using co-
occurences based method and NLP-based methods
and text mining tools are discussed [4]. Fifth
triglyceride mia by anthropometric
measures based on data mining. Many diseases can be
predicted by the change in hyper triglyceride mia it
varies according to age and gender [6]. Sixth and final
technique is multilayer classifier for disease
prediction. A huge number of prediction models can
be build from data mining techniques. Here the
concept of machine learning is extended. Machine
learning are additionally classified into supervised and
Pathway Based Approach:
Through gene expression diseases can be predicted
where samples of genes are sketched as specimen of
tates for understanding the disease
phenotype. The computation of gene expression
estimate disease outcome. By using feature selection
algorithm we can pick a batch of genes which are
differently expressed. In network based approaches
methods suspect that all cellular
element that are owned to the identical topological,
working or disease module have a inflated chance of
having same disease. Greedy search is executed over
(People of Information Network) to pinpoint a
e modules whose mean countenance is
2. International Journal of Trend in Scientific Research and Development (IJTSRD) ISSN: 2456
@ IJTSRD | Available Online @ www.ijtsrd.com
supreme. PIN data is usually undependable and
clattering and it also has a high false positive rate. In
pathway based approaches biological pathways are
unreliable and organised troupe of molecular
interaction network. Pathway level disease
classification approach based on hyper-
where given a microarray gene expression portrait and
a number of biological pathways/gene
classification exactness of each pathway/gene
assessed by using only the adherent genes in the
pathway. By using psoriasis and breast cancer datasets
prediction accuracies are greater than 85% superior
than sets of genes that are selected unplanned. Hyper
box disease classification (Hyper DC) analyze disease
specimen accurately when comparing with other
prominent classification methodologies considering to
SNR classification rates. Hyper DC show inflated
SNR. Actual strength of Hyper DC rely
power. Main advantage of Hyper DC is it is flexible
[1].
2.2. Hybrid Prediction Model with Missing Value
Imputation:
Exact prediction in huge number of missing values in
dataset is a tough task. Many hybrid models to face
this problem they have removed the missing instances
from dataset which is commercially known as case
deletion. Hybrid prediction model with Missing Value
Imputation (HPM-MI) scrutinize different imputation
technique by applying simple k-means clustering and
use the best one to dataset. Missing values happen
because of many reasons due to mistake in manual
data, equipment mistakes, or inaccurate
measurements. Missing values can cause many
problems like loss of efficiency, convolutions
managing and examining data. It may also lead to bias
decisions. We have analyzed 11 Missing Value
Imputation techniques they are case deletion, Most
Common Method (MC), Concept Most Common
(CMC), K-Nearest Neighbor (KNNI
Imputation with K-Nearest Neighbor
Means Clustering Imputation (KMI), Imputation with
Fuzzy K-Means Clustering (FKMI), Support Vector
Machines Imputations (SVMI), Singular Value
Decomposition Imputation (SVDI), Local Least
Squares Imputation (LLSI), Matrix Factorisation,
Selecting best MVI method is based on accuracy it is
attained. We have selected clustering as beginning for
selection of best imputation technique. K
Clustering seperates datasets into groups so that
instances in one set are similar to each other and as
dissimilar as possible from the objects in other
International Journal of Trend in Scientific Research and Development (IJTSRD) ISSN: 2456
www.ijtsrd.com | Volume – 2 | Issue – 6 | Sep-Oct 2018
supreme. PIN data is usually undependable and
clattering and it also has a high false positive rate. In
pathway based approaches biological pathways are
unreliable and organised troupe of molecular
athway level disease
-box principles
where given a microarray gene expression portrait and
a number of biological pathways/gene set the
classification exactness of each pathway/gene sets is
dherent genes in the
pathway. By using psoriasis and breast cancer datasets
prediction accuracies are greater than 85% superior
than sets of genes that are selected unplanned. Hyper
(Hyper DC) analyze disease
when comparing with other
prominent classification methodologies considering to
SNR classification rates. Hyper DC show inflated
ctual strength of Hyper DC rely on illustrative
power. Main advantage of Hyper DC is it is flexible
Missing Value
Exact prediction in huge number of missing values in
dataset is a tough task. Many hybrid models to face
this problem they have removed the missing instances
from dataset which is commercially known as case
deletion. Hybrid prediction model with Missing Value
MI) scrutinize different imputation
means clustering and
use the best one to dataset. Missing values happen
because of many reasons due to mistake in manual
data, equipment mistakes, or inaccurate
measurements. Missing values can cause many
, convolutions in
managing and examining data. It may also lead to bias
decisions. We have analyzed 11 Missing Value
they are case deletion, Most
Concept Most Common
Nearest Neighbor (KNNI), Weighted
(WKNN). K-
(KMI), Imputation with
(FKMI), Support Vector
(SVMI), Singular Value
(SVDI), Local Least
(LLSI), Matrix Factorisation,
Selecting best MVI method is based on accuracy it is
attained. We have selected clustering as beginning for
of best imputation technique. K-Means
Clustering seperates datasets into groups so that
instances in one set are similar to each other and as
dissimilar as possible from the objects in other
groups. CMC method has given less number of
wrongly classified instances in diabetes as well as in
hepatitis dataset. Case deletion is best in hepatitis
dataset [2].
2.3. CCAR: With Item set Constraints
Class Association Rules (CAR's) are used to construct
a classification model for prediction and narrate
association between item set and class labels. The
initiation of mining association rules with item
constraints,3 major approach has been proposed. First
method is post-processing method, first finds repeated
item sets using a priori algorithm, second
pre-processing method which filters out the ones
which do not convince the item
third approach is constrained item
tries to assimilate the item set constraints into original
mining process because it need
frequent item set that pleases the constraints .In post
processing approach CAR-mining algorithm is used.
This strategy is not very efficient because all CAR's
must be generated and frequently a large number of
candidate CAR's need to be tested. Advantage of pre
processing approach is the size of the filtered dataset
is very much lesser than the actual dataset. So the
mining time can be notably reduced. The authors
manifested that their method is higher than the post
processing method. In our proposed method the item
set constraint is thrust as intense "inside" estimation
as possible. Instead of using all rules, only rules
which please the item set constraints are
speed up the process. Proposed algorithm for mining
CAR's is tree structure which includes only nodes
having constrained item set and two theorems for
soon trimming infrequent item
2.4. TEXT MINING:
One of the major dominant entry point to scientific
literature sources for biomedical research is pub
which give entry to more than 24 million scientific
literature. Fetching of relevant information from
literature database fusing these information with
experimental output is time taking and it also requires
some careful attention so text mining is introduc
Text mining reply to many research questions
extending from the discovery of drug targets to drug
repositioning. Definition of text mining by Marti
Hearst is "the discovery by computer of new,
previously unknown information unknown
information from different written resources, to reveal
otherwise 'hidden' meanings". The first and the
foremost step in text mining is to fetch suitable textual
International Journal of Trend in Scientific Research and Development (IJTSRD) ISSN: 2456-6470
Oct 2018 Page: 735
CMC method has given less number of
nstances in diabetes as well as in
hepatitis dataset. Case deletion is best in hepatitis
set Constraints:
(CAR's) are used to construct
a classification model for prediction and narrate
set and class labels. The
initiation of mining association rules with item set
constraints,3 major approach has been proposed. First
processing method, first finds repeated
priori algorithm, second approach is
processing method which filters out the ones
which do not convince the item set constraint, the
third approach is constrained item set filtering which
set constraints into original
mining process because it needs to generate only the
set that pleases the constraints .In post-
mining algorithm is used.
This strategy is not very efficient because all CAR's
must be generated and frequently a large number of
o be tested. Advantage of pre-
processing approach is the size of the filtered dataset
is very much lesser than the actual dataset. So the
mining time can be notably reduced. The authors
manifested that their method is higher than the post-
. In our proposed method the item
set constraint is thrust as intense "inside" estimation
as possible. Instead of using all rules, only rules
constraints are formed to
Proposed algorithm for mining
ree structure which includes only nodes
set and two theorems for
soon trimming infrequent item sets [3].
One of the major dominant entry point to scientific
literature sources for biomedical research is pub Med
hich give entry to more than 24 million scientific
literature. Fetching of relevant information from
literature database fusing these information with
experimental output is time taking and it also requires
some careful attention so text mining is introduced.
Text mining reply to many research questions
extending from the discovery of drug targets to drug
repositioning. Definition of text mining by Marti
Hearst is "the discovery by computer of new,
previously unknown information unknown
ferent written resources, to reveal
otherwise 'hidden' meanings". The first and the
foremost step in text mining is to fetch suitable textual
3. International Journal of Trend in Scientific Research and Development (IJTSRD) ISSN: 2456
@ IJTSRD | Available Online @ www.ijtsrd.com
resources for a given subject of interest. This process
is called as information retrieval. After IR the
resulting document set can be examined by search
algorithms for the occurrence of particular keywords
of interest. For example a particular gene should be
acknowledged in the text not only by its gene symbol,
but also by the synonyms and previous names this is
called as NER concept. After IR and NER technical
algorithms can be used to discover links between
concepts in the text. Recently the most used
approaches to extract information from text are co
occurrence based methods and Natural Processing
(NLP) based methods. Comparison of these 2
approaches NLP based method has more advantages.
Some of the applications of TM to biomedical
problems are genome and gene expression annotation,
drug repositioning, adverse events, electronic health
records, domain specific databases[4].Proteins are the
molecules that ease most biological process in a cell.
Some of the text mining tools are Bio
(Biological Research Assistant for Text Mining, eFIP
(Extracting Functional impact of phosphorylati
FACTA+(Finding Associated Conccepts with text
analysis), Gene Ways, Hit Predict, In Print, I2D
logo us Interaction Database), iHOP
Hyperlinked Over Project), IMID(Integrated
Molecular Interaction Database), Negatome, open
DMAP, PCorral (Protein Corral), PIE the
search, PPIExtractor, PPIfinder, PPLook, STRING[5].
2.5 Hyper triglyceride mi a From
Anthropometric Measures:
The excellent indicator for the prediction of hyper
triglyceride mi a from anthropometric measures of
body shape which remains a matter of debate. A total
of 5517 subjects have participated in this cross
sectional study (3675 women and 1842 men) aged 20
90 years. When the subject is standing the
circumference of 8 particular sites were measured
using a flexible non-elastic tape. The BMI wa
calculated as weight in kilograms divided by square of
the height in meters. Various circumference
measurements are Forehead Circumference
Neck Circumference (NC), Axilliary Circumference
(AC), Chest Circumference (CC), Pelvic
Circumference (PC), Rib Circumference
Circumference (WC), Hip Circumference
and female data are divided seperately because the
difference in body shape with aging may vary
according to sex. Waist to Hip Ratio (WHR) were the
strongest predictors of hyper triglyceride
Indian men. WHtR (Waist to Height Ratio) was the
International Journal of Trend in Scientific Research and Development (IJTSRD) ISSN: 2456
www.ijtsrd.com | Volume – 2 | Issue – 6 | Sep-Oct 2018
resources for a given subject of interest. This process
is called as information retrieval. After IR the
document set can be examined by search
algorithms for the occurrence of particular keywords
of interest. For example a particular gene should be
acknowledged in the text not only by its gene symbol,
but also by the synonyms and previous names this is
ed as NER concept. After IR and NER technical
algorithms can be used to discover links between
concepts in the text. Recently the most used
approaches to extract information from text are co-
occurrence based methods and Natural Processing
ds. Comparison of these 2
approaches NLP based method has more advantages.
Some of the applications of TM to biomedical
problems are genome and gene expression annotation,
drug repositioning, adverse events, electronic health
ases[4].Proteins are the
molecules that ease most biological process in a cell.
Some of the text mining tools are Bio RAT
(Biological Research Assistant for Text Mining, eFIP
(Extracting Functional impact of phosphorylati on),
ccepts with text
Print, I2D (Inter
us Interaction Database), iHOP (information
Hyperlinked Over Project), IMID(Integrated
Molecular Interaction Database), Negatome, open
(Protein Corral), PIE the search, Poly
search, PPIExtractor, PPIfinder, PPLook, STRING[5].
Hyper triglyceride mi a From
The excellent indicator for the prediction of hyper
a from anthropometric measures of
of debate. A total
of 5517 subjects have participated in this cross-
sectional study (3675 women and 1842 men) aged 20-
90 years. When the subject is standing the
circumference of 8 particular sites were measured
elastic tape. The BMI was
calculated as weight in kilograms divided by square of
the height in meters. Various circumference
measurements are Forehead Circumference (FC),
(NC), Axilliary Circumference
(CC), Pelvic
ib Circumference (RC), Waist
(WC), Hip Circumference (HC). Male
and female data are divided seperately because the
difference in body shape with aging may vary
(WHR) were the
triglyceride mi a in
(Waist to Height Ratio) was the
best predictor of hyper triglyceride
Rib to Forehead Circumference ratio (RFCR) was the
best predictor in men. However in the age group of
20-50 years the best predictor o
a were rib circumference and WHtR in 51
group in women and RFCR in 20
BMI in 51-90 year group men. The best predictor of
hyper triglyceride mi a varies according to gender and
age [6].
2.6. HMV: Medical
Framework:
Decision Support System(DSS) help decision makers
to collect and interpret information and construct a
foundation for decision making . Medical DSS play
an important role in medical by guiding doctors with
clinical decisions. Data mining in medical area is a
process of uncovering hidden patterns and
information from huge medical datasets, examine and
use them for disease prediction. The proposed HMV
overcome the disadvantages of conventional
performance by using a group of seven
classifiers. HMV framework removes noise from
medical dataset by using clustering approach. The
selection of optimal set of classifiers is an crucial step.
The HMV satisfied 2 conditions accuracy and
prediction diversity to achieve high quali
ensemble framework is done on 2 heart disease
dataset, 2 diabetes dataset, 2 liver disease dataset
hepatitis dataset, 1 park in son’s
these disease datasets HMV has produced highest
accuracy in comparison with other
Multilayer classifier is introduced to enhance the
prediction. Predictions done by proposed DSS is
parallel with prediction performed by panel of
doctors. Heterogeneous classifier ensemble model is
used by combining different type of classifier
achieved a high level of diversity. Naive Bayes
is probabilistic classifier which shows higher
prediction accuracy and classification
evaluation methods are decision trees, QDA
(Quadratic Discriminant Analysis), LR
Regression), SVM, and Bayesian
evaluation method is KNN combining lazy and eager
evaluation algorithm (hybrid approach) results in
overcoming the limitation of both eager and lazy
methods. Eager method may suffer of missing rule
problem when there is no matching exists. In this
scenario it adopts default prediction. Proposed
framework is constructed on three modules. The first
module is based on data acquisition and pre
processing which gathers data from different data
International Journal of Trend in Scientific Research and Development (IJTSRD) ISSN: 2456-6470
Oct 2018 Page: 736
triglyceride mi a in women.
Rib to Forehead Circumference ratio (RFCR) was the
in men. However in the age group of
50 years the best predictor of hyper triglyceride mi
a were rib circumference and WHtR in 51-90 year
group in women and RFCR in 20-50 years group and
90 year group men. The best predictor of
a varies according to gender and
Decision Support
Decision Support System(DSS) help decision makers
to collect and interpret information and construct a
foundation for decision making . Medical DSS play
an important role in medical by guiding doctors with
ta mining in medical area is a
process of uncovering hidden patterns and
information from huge medical datasets, examine and
use them for disease prediction. The proposed HMV
overcome the disadvantages of conventional
using a group of seven heterogeneous
classifiers. HMV framework removes noise from
medical dataset by using clustering approach. The
selection of optimal set of classifiers is an crucial step.
The HMV satisfied 2 conditions accuracy and
prediction diversity to achieve high quality. The HMV
ensemble framework is done on 2 heart disease
dataset, 2 diabetes dataset, 2 liver disease dataset, 1
son’s disease dataset. In all
these disease datasets HMV has produced highest
with other classifiers.
Multilayer classifier is introduced to enhance the
prediction. Predictions done by proposed DSS is
parallel with prediction performed by panel of
doctors. Heterogeneous classifier ensemble model is
used by combining different type of classifiers and
achieved a high level of diversity. Naive Bayes (NB)
probabilistic classifier which shows higher
classification. Eager
evaluation methods are decision trees, QDA
(Quadratic Discriminant Analysis), LR (Linear
Bayesian classification. Lazy
evaluation method is KNN combining lazy and eager
(hybrid approach) results in
overcoming the limitation of both eager and lazy
methods. Eager method may suffer of missing rule
s no matching exists. In this
scenario it adopts default prediction. Proposed
framework is constructed on three modules. The first
module is based on data acquisition and pre
processing which gathers data from different data
4. International Journal of Trend in Scientific Research and Development (IJTSRD) ISSN: 2456
@ IJTSRD | Available Online @ www.ijtsrd.com
warehouse and pre process them. In second module
individual classifiers training is executed on the
training set and they are used for predicting unknown
class labels for test instances. The third module is
prediction and evaluation of proposed ensemble
framework. It is also performed on real time blood CP
datasets which was taken from PIMS hospital to
discover healthy and disease patients and the result of
the samples again showed higher disease prediction
accuracy it also guides practitioners and patients for
the prediction of disease based on the symptoms of
disease [7].
3. COMPARITIVE STUDY:
Predictions Advantage Disadvantage
1st
prediction
High
accuracy consuming
2nd
prediction
Accuracy
Imbalanced
classification
3rd
prediction
Efficiency
Duplicate
content
4th
prediction
Efficient uncertain
5th
prediction
Simple
No cause effect
relationship
6th
prediction
High
accuracy
Inflexible
Table 1 com paritive study on various
4. CONCLUSION:
Accuracy plays an requisite role in the medical field
as it is related to the life of an individual. In the
analysis of these six prediction techniques HMV:
DSS using multilayer classifier is considered to be the
best prediction technique among these because it has
higher prediction accuracy and it also help and guide
practitioners in predicting the disease.
International Journal of Trend in Scientific Research and Development (IJTSRD) ISSN: 2456
www.ijtsrd.com | Volume – 2 | Issue – 6 | Sep-Oct 2018
In second module
individual classifiers training is executed on the
training set and they are used for predicting unknown
class labels for test instances. The third module is
prediction and evaluation of proposed ensemble
n real time blood CP
datasets which was taken from PIMS hospital to
discover healthy and disease patients and the result of
the samples again showed higher disease prediction
accuracy it also guides practitioners and patients for
based on the symptoms of
Disadvantage
Time
consuming
Imbalanced
classification
Duplicate
content
uncertain
No cause effect
relationship
Inflexible
paritive study on various predictions
Accuracy plays an requisite role in the medical field
the life of an individual. In the
analysis of these six prediction techniques HMV:
DSS using multilayer classifier is considered to be the
best prediction technique among these because it has
higher prediction accuracy and it also help and guide
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