The clinical decision support system using the case based reasoning (CBR) methodology of Artificial Intelligence (AI) presents a foundation for a new technology of building intelligent computer aided diagnoses systems. This Technology directly addresses the problems found in the traditional Artificial Intelligence (AI) techniques, e.g. the problems of knowledge acquisition, remembering, robust and maintenance. In this paper, we have used the Case Based Reasoning methodology to develop a clinical decision support system prototype for supporting diagnosis of occupational lung diseases. 127 cases were collected for 14 occupational chronic lung diseases, which contains 26 symptoms. After removing the duplicated cases from the database, the system has trained set of 47 cases for Indian Lung patients. Statistical analysis has been done to determine the importance values of the case features. The retrieval strategy using nearest-neighbor approaches is investigated. The results indicate that the nearest neighbor approach has shown the encouraging outcome, used as retrieval strategy. A Consultant Pathologist’s interpretation was used to evaluate the system. Results for Sensitivity, Specificity, Positive Prediction Value and the Negative Prediction Value are 95.3%, 92.7%, 98.6% and 81.2% respectively. Thus, the result showed that the system is capable of assisting an inexperience pathologist in making accurate, consistent and timely diagnoses, also in the study of diagnostic protocol, education, self-assessment, and quality control. In this paper, clinical decision support system prototype is developed for supporting diagnosis of occupational lung diseases from their symptoms and signs through employing Microsoft Visual Basic .NET 2005 along with Microsoft SQL server 2005 environment with the advantage of Object Oriented Programming technology
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.
IRJET- Disease Prediction and Doctor Recommendation SystemIRJET Journal
This document proposes a disease prediction and doctor recommendation system that uses machine learning and natural language processing. It uses a Naive Bayes classifier to predict the likelihood of diseases based on patient symptoms and medical data. It then recommends doctors for the predicted disease by analyzing reviews with CoreNLP and filtering by location, cost, experience or reviews. The system aims to help users receive accurate diagnoses and treatment recommendations efficiently. Future work could involve expanding the types of diseases and doctors covered as well as collecting more real-world patient and review data to improve accuracy.
This document discusses using data mining classifiers and attribute reduction techniques to predict chronic kidney disease (CKD) more accurately and efficiently. It first provides background on CKD and the need for early detection. It then discusses data mining, classification algorithms, attribute selection filters and wrappers. The document analyzes several studies that predicted CKD using techniques like decision trees, SVM and Naive Bayes. It describes the dataset used from the UCI repository and evaluation metrics. The results section compares J48, Decision Tree and IBK classifiers with and without attribute reduction using CfsSubsetEval, ClassifierSubsetEval and WrapperSubsetEval. Attribute reduction improved accuracy, especially for IBK which achieved 100% accuracy with 72% fewer attributes.
DATA MINING CLASSIFICATION ALGORITHMS FOR KIDNEY DISEASE PREDICTION IJCI JOURNAL
Data mining is a non-trivial process of categorizing valid, novel, potentially useful and ultimately understandable patterns in data. In terms, it accurately state as the extraction of information from a huge database. Data mining is a vital role in several applications such as business organizations, educational institutions, government sectors, health care industry, scientific and engineering. . In the health care
industry, the data mining is predominantly used for disease prediction. Enormous data mining techniques are existing for predicting diseases namely classification, clustering, association rules, summarizations, regression and etc. The main objective of this research work is to predict kidney diseases using classification algorithms such as Naïve Bayes and Support Vector Machine. This research work mainly
focused on finding the best classification algorithm based on the classification accuracy and execution time performance factors. From the experimental results it is observed that the performance of the SVM is better than the Naive Bayes classifier algorithm.
In this talk, Hector will cover some recent advances in applying machine learning to the field of healthcare. A brief overview of deep learning and its applications in healthcare such as diagnostics, care management, decision support and personalized medicine. There will be deeper dives into specific topics such as machine learning on electronic health records and analyzing EEGs.
IRJET - Chronic Kidney Disease Prediction using Data Mining and Machine LearningIRJET Journal
This document discusses predicting chronic kidney disease through data mining and machine learning techniques. It examines using KNN, SVM, and ensemble models on a dataset of 400 patient records with 24 attributes related to chronic kidney disease. For data mining, SVM with an RBF kernel achieved 87% accuracy. For machine learning, KNN and SVM ensemble achieved over 92% accuracy. The document reviews several related studies applying classification algorithms like decision trees, neural networks, and Naive Bayes to chronic kidney disease prediction and their limitations. It then describes the KNN algorithm and its application to this problem in more detail.
IRJET- The Prediction of Heart Disease using Naive Bayes ClassifierIRJET Journal
This document presents a study on using the Naive Bayes classification technique to predict heart disease risk levels based on patient attributes. The study uses a heart disease dataset containing records of patients with 13 attributes each. The Naive Bayes classifier is applied to both the training dataset of 457 records and testing dataset of 88 records. The performance is evaluated based on various metrics like accuracy, precision, recall, F-measure etc. On the training data, the Naive Bayes classifier achieved 96.28% accuracy and on the testing data it achieved 98.86% accuracy, demonstrating it can accurately predict heart disease risk levels.
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.
IRJET- Disease Prediction and Doctor Recommendation SystemIRJET Journal
This document proposes a disease prediction and doctor recommendation system that uses machine learning and natural language processing. It uses a Naive Bayes classifier to predict the likelihood of diseases based on patient symptoms and medical data. It then recommends doctors for the predicted disease by analyzing reviews with CoreNLP and filtering by location, cost, experience or reviews. The system aims to help users receive accurate diagnoses and treatment recommendations efficiently. Future work could involve expanding the types of diseases and doctors covered as well as collecting more real-world patient and review data to improve accuracy.
This document discusses using data mining classifiers and attribute reduction techniques to predict chronic kidney disease (CKD) more accurately and efficiently. It first provides background on CKD and the need for early detection. It then discusses data mining, classification algorithms, attribute selection filters and wrappers. The document analyzes several studies that predicted CKD using techniques like decision trees, SVM and Naive Bayes. It describes the dataset used from the UCI repository and evaluation metrics. The results section compares J48, Decision Tree and IBK classifiers with and without attribute reduction using CfsSubsetEval, ClassifierSubsetEval and WrapperSubsetEval. Attribute reduction improved accuracy, especially for IBK which achieved 100% accuracy with 72% fewer attributes.
DATA MINING CLASSIFICATION ALGORITHMS FOR KIDNEY DISEASE PREDICTION IJCI JOURNAL
Data mining is a non-trivial process of categorizing valid, novel, potentially useful and ultimately understandable patterns in data. In terms, it accurately state as the extraction of information from a huge database. Data mining is a vital role in several applications such as business organizations, educational institutions, government sectors, health care industry, scientific and engineering. . In the health care
industry, the data mining is predominantly used for disease prediction. Enormous data mining techniques are existing for predicting diseases namely classification, clustering, association rules, summarizations, regression and etc. The main objective of this research work is to predict kidney diseases using classification algorithms such as Naïve Bayes and Support Vector Machine. This research work mainly
focused on finding the best classification algorithm based on the classification accuracy and execution time performance factors. From the experimental results it is observed that the performance of the SVM is better than the Naive Bayes classifier algorithm.
In this talk, Hector will cover some recent advances in applying machine learning to the field of healthcare. A brief overview of deep learning and its applications in healthcare such as diagnostics, care management, decision support and personalized medicine. There will be deeper dives into specific topics such as machine learning on electronic health records and analyzing EEGs.
IRJET - Chronic Kidney Disease Prediction using Data Mining and Machine LearningIRJET Journal
This document discusses predicting chronic kidney disease through data mining and machine learning techniques. It examines using KNN, SVM, and ensemble models on a dataset of 400 patient records with 24 attributes related to chronic kidney disease. For data mining, SVM with an RBF kernel achieved 87% accuracy. For machine learning, KNN and SVM ensemble achieved over 92% accuracy. The document reviews several related studies applying classification algorithms like decision trees, neural networks, and Naive Bayes to chronic kidney disease prediction and their limitations. It then describes the KNN algorithm and its application to this problem in more detail.
IRJET- The Prediction of Heart Disease using Naive Bayes ClassifierIRJET Journal
This document presents a study on using the Naive Bayes classification technique to predict heart disease risk levels based on patient attributes. The study uses a heart disease dataset containing records of patients with 13 attributes each. The Naive Bayes classifier is applied to both the training dataset of 457 records and testing dataset of 88 records. The performance is evaluated based on various metrics like accuracy, precision, recall, F-measure etc. On the training data, the Naive Bayes classifier achieved 96.28% accuracy and on the testing data it achieved 98.86% accuracy, demonstrating it can accurately predict heart disease risk levels.
Comparative Study of Classification Method on Customer Candidate Data to Pred...IJECEIAES
Leasing vehicles are a company engaged in the field of vehicle loans. Purchase by way of credit becomes a mainstay because it can attract potential customers to generate more profit. But if there is a mistake in approving a customer candidate, the risk of stalled credit payments can happen. To minimize the risk, it can be applied the certain data mining technique to predict the future behavior of the customers. In this study, it is explored in some data mining techniques such as C4.5 and Naive Bayes for this purpose. The customer attributes used in this study are: salary, age, marital status, other installments and worthiness. The experiments are performed by using the Weka software. Based on evaluation criteria, i.e. accuracy, C4.5 algorithm outperforms compared to Naive Bayes. The percentage split experiment scenarios provide the precision value of 89.16% and the accuracy value of 83.33% wheres the cross validation experiment scenarios give the higher accuracy values of all used k-fold. The C4.5 experiment results also confirm that the most influential instant data attribute in this research is the salary.
A Survey on Heart Disease Prediction Techniquesijtsrd
Heart disease is the main reason for a huge number of deaths in the world over the last few decades and has evolved as the most life threatening disease. The health care industry is found to be rich in information. So, there is a need to discover hidden patterns and trends in them. For this purpose, data mining techniques can be applied to extract the knowledge from the large sets of data. Many researchers, in recent times have been using several machine learning techniques for predicting the heart related diseases as it can predict the disease effectively. Even though a machine learning technique proves to be effective in assisting the decision makers, still there is a scope for developing an accurate and efficient system to diagnose and predict the heart diseases thereby helping doctors with ease of work. This paper presents a survey of various techniques used for predicting heart disease and reviews their performance. G. Niranjana | Dr I. Elizabeth Shanthi "A Survey on Heart Disease Prediction Techniques" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-2 , February 2021, URL: https://www.ijtsrd.com/papers/ijtsrd38349.pdf Paper Url: https://www.ijtsrd.com/computer-science/other/38349/a-survey-on-heart-disease-prediction-techniques/g-niranjana
PREDICTIVE ANALYTICS IN HEALTHCARE SYSTEM USING DATA MINING TECHNIQUEScscpconf
The health sector has witnessed a great evolution following the development of new computer technologies, and that pushed this area to produce more medical data, which gave birth to multiple fields of research. Many efforts are done to cope with the explosion of medical data on one hand, and to obtain useful knowledge from it on the other hand. This prompted researchers to apply all the technical innovations like big data analytics, predictive analytics, machine learning and learning algorithms in order to extract useful knowledge and help in making decisions. With the promises of predictive analytics in big data, and the use of machine learning
algorithms, predicting future is no longer a difficult task, especially for medicine because predicting diseases and anticipating the cure became possible. In this paper we will present an overview on the evolution of big data in healthcare system, and we will apply a learning algorithm on a set of medical data. The objective is to predict chronic kidney diseases by using Decision Tree (C4.5) algorithm.
Framework for comprehensive enhancement of brain tumor images with single-win...IJECEIAES
Usage of grayscale format of radiological images is proportionately more as compared to that of colored one. This format of medical image suffers from all the possibility of improper clinical inference which will lead to error-prone analysis in further usage of such images in disease detection or classification. Therefore, we present a framework that offers single-window operation with a set of image enhancing algorithm meant for further optimizing the visuality of medical images. The framework performs preliminary pre-processing operation followed by implication of linear and non-linear filter and multi-level image enhancement processes. The significant contribution of this study is that it offers a comprehensive mechanism to implement the various enhancement schemes in highly discrete way that offers potential flexibility to physical in order to draw clinical conclusion about the disease being monitored. The proposed system takes the case study of brain tumor to implement to testify the framework.
PERFORMANCE OF DATA MINING TECHNIQUES TO PREDICT IN HEALTHCARE CASE STUDY: CH...ijdms
This document discusses applying machine learning algorithms to predict chronic kidney disease. It:
1) Applied three algorithms (C4.5 decision tree, SVM, and Bayesian Network) to a chronic kidney disease dataset containing 400 patients and 24 attributes to classify patients as having chronic kidney disease or not.
2) Found that the C4.5 decision tree algorithm had the best performance based on accuracy (63%), error rate (0.37), kappa statistic (0.97), and other evaluation metrics. SVM and Bayesian Network performance was lower.
3) Concludes C4.5 decision tree is the most efficient algorithm for predicting chronic kidney disease based on this medical dataset.
The Analysis of Performace Model Tiered Artificial Neural Network for Assessm...IJECEIAES
The assessment model of coronary heart disease is so much developed in line with the development of information technology, particularly the field of artificial intelligence. Unfortunately, the assessment models developed mostly do not use such an approach made by the clinician, that is the tiered approach. This makes the assessment process should conduct a thorough examination. This study aims to analyze the performance of a tiered model assessment. The assessment system is divided into several levels, with reference to the stages of the inspection procedure.The method used for each level is, preprocessing, building architecture artificial neural network (ANN), conduct training using the Levenberg-Marquardt algorithm and one step secant, as well as testing the system. The test results showed the influence of each level, both when the output level of the previous positive or negative, were tested back at the next level. The effect indicates that the level above gives performance improvement and or strengthens the performance at the previous level.
Existing model uses structured data to predict the patients of either high risk or low risk.
But for a complex disease, structured data is not a good way to describe the disease.
We propose a new convolutional neural network (CNN)-based multimodal disease risk prediction algorithm using structured and unstructured data from hospital.
In this paper, we mainly focus on the risk prediction of cerebral infarction.
IRJET - Classification and Prediction for Hospital Admissions through Emergen...IRJET Journal
This document discusses using machine learning techniques to predict hospital admissions from emergency departments in order to improve patient flow and reduce overcrowding. It compares the performance of logistic regression and random forest algorithms on a dataset. Logistic regression identified several factors related to admissions including age, arrival mode, previous admissions. Random forests had the lowest accuracy. Predictive models could allow advance planning of resources to prevent bottlenecks. Future work involves exploring additional machine learning methods.
Survey on data mining techniques in heart disease predictionSivagowry Shathesh
This document summarizes research on using data mining techniques to predict heart disease. It discusses previous work using classification, clustering, association rule mining and other techniques on several heart disease datasets. Classification algorithms like naive bayes, decision trees and neural networks have been widely used with naive bayes found to often provide the best performance. Feature selection and attribute reduction are also examined. The document provides an overview of the key steps and techniques in medical data mining and predictive analysis for heart disease.
IRJET- Chronic Kidney Disease Prediction based on Naive Bayes TechniqueIRJET Journal
This document discusses using a Naive Bayes technique to predict chronic kidney disease (CKD) based on patient data. It begins by introducing data mining and its applications in healthcare to extract useful information from large datasets. It then reviews literature on using classification algorithms like Naive Bayes for disease detection. Next, it describes the limitations of existing manual CKD prediction systems. The proposed system would automate CKD prediction using a Naive Bayes classifier to help doctors diagnose the disease which affects many worldwide. The methodology involves collecting clinical data, pre-processing it, then applying the Naive Bayes technique to extract patterns and predict CKD.
Hybrid Technique for Associative Classification of Heart DiseasesJagdeep Singh Malhi
The document discusses a hybrid technique for associative classification of heart diseases using data mining. It summarizes existing classification and association rule mining algorithms applied to heart disease data. The author aims to improve accuracy by generating classification association rules efficiently and integrating classification with association rule mining. The proposed approach is implemented in Weka to extract rules from a heart disease dataset using Apriori and FP-Growth algorithms. The rules are used to classify patients and evaluate the performance compared to other methods.
Deep learning in healthcare: Oppotunities and challenges with Electronic Medi...Thien Q. Tran
Interested in deep learning for healthcare has grown strongly recent years besides with the successes in other domains such as Computer Vision, Natural Language Processing, Speech Recognition and so forth. This talk will try to give a brief look into the recent effort of research in deep learning for healthcare. Especially, this talk focuses on the opportunities and challenges in using electronic health records (EHR) data, which is one of the most important data sources in healthcare domain.
Heart Disease Prediction Using Data Mining TechniquesIJRES Journal
There are huge amounts of data in the medical industry which is not processed properly and hence cannot be used effectively in making decisions. We can use data mining techniques to mine these patterns and relationships. This research has developed a prototype Heart Disease Prediction using data mining techniques, namely Neural Network, K-Means Clustering and Frequent Item Set Generation. Using medical profiles such as age, sex, blood pressure and blood sugar it can predict the likelihood patients getting a heart disease. It enables significant knowledge, e.g. patterns, relationships between medical factors related to heart disease to be established. Performance of these techniques is compared through sensitivity, specificity and accuracy. It has been observed that Artificial Neural Networks outperform K Means clustering in all the parameters i.e. Sensitivity, Specificity and Accuracy.
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
This document summarizes a research paper that proposes a machine learning approach to identify disease-treatment relationships from biomedical text. It extracts sentences mentioning diseases and treatments from medical publications and classifies the semantic relationships between them. The researchers evaluate their methodology on a dataset of sentences annotated with diseases, treatments and their relationships. Their results show the machine learning models can reliably extract this information and outperform previous methods on the same data. The proposed approach could be integrated into applications to disseminate healthcare information from published literature to medical professionals and patients.
Survey on data mining techniques in heart disease predictionSivagowry Shathesh
This document describes a study on applying data mining techniques to analyze and predict heart disease. It discusses how data mining can extract valuable knowledge from healthcare data. The study uses several data mining techniques like decision trees, naive Bayes classification, clustering, and association rule mining on heart disease datasets from UC Irvine to predict heart disease. Experimental results show that multilayer neural networks and classification techniques like naive Bayes had higher prediction accuracy compared to other methods.
Heart Disease Prediction using Data MiningIRJET Journal
This document describes a study that uses data mining techniques like neural networks and genetic algorithms to predict heart disease based on major risk factors. The proposed system initializes neural network weights using a genetic algorithm for feature selection and classification to build an intelligent clinical decision support system. It analyzes heart disease risk factors like age, cholesterol, blood pressure, smoking status and diabetes using a neuro-fuzzy model optimized with a genetic algorithm. The system is able to predict heart disease with 89% accuracy and can help detect the disease early to improve treatment outcomes.
MULTI-CRITERIA DECISION SUPPORT GUIDED BY CASE-BASED REASONINGcsandit
Many systems based on knowledge, especially expert systems for medical decision support have
been developed. Only systems are based on production rules, and cannot learn and evolve only
by updating them. In addition, taking into account several criteria induces an exorbitant number
of rules to be injected into the system. It becomes difficult to translate medical knowledge or a
support decision as a simple rule. Moreover, reasoning based on generic cases became classic
and can even reduce the range of possible solutions. To remedy that, we propose an approach
based on using a multi-criteria decision guided by a case-based reasoning (CBR) approach.
Nilesh Bhaskarrao Bahadure presents information on biomedical image processing and signal analysis. The document discusses biomedical signals, their origin and dynamics, and processing techniques. It explains that physiological processes produce signals that can provide information about health and disease states. Advanced signal processing is needed to extract clinically relevant data from complex biomedical signals. The document also describes computer-aided diagnosis systems, which apply computer technology to medical imaging to assist physicians' clinical decision making and improve diagnostic accuracy.
Early Identification of Diseases Based on Responsible Attribute using Data Mi...IRJET Journal
This document describes a proposed method for early identification of diseases using data mining and classification techniques. It begins with an introduction to classification and discusses how it is commonly used in healthcare for tasks like predicting patient risk levels. It then reviews related literature applying classification methods to diseases like heart disease and diabetes. The document outlines the problem of selecting the best classification technique for a given healthcare dataset. It proposes an architecture and method for disease prediction that assigns recommended values to attributes and classifies unknown data based on calculating totals. The method is experimentally analyzed using a heart disease dataset, and its accuracy is compared to Bayesian classification. In conclusion, the proposed method seeks to reduce attributes and complexity while accurately classifying patient data for early disease identification.
This document discusses using data mining techniques like association rule mining and improved apriori algorithm with fuzzy logic to develop an expert system that can predict the risk of osteoporosis based on a patient's clinical data and history. It aims to help doctors make more informed decisions early on to prevent osteoporosis. The system would find relationships between various risk factors and diagnose osteoporosis severity to identify at-risk patients before costly tests. Literature on using different algorithms like decision trees and neural networks for medical diagnosis and predicting osteoporosis risk is also reviewed.
Comparative Study of Classification Method on Customer Candidate Data to Pred...IJECEIAES
Leasing vehicles are a company engaged in the field of vehicle loans. Purchase by way of credit becomes a mainstay because it can attract potential customers to generate more profit. But if there is a mistake in approving a customer candidate, the risk of stalled credit payments can happen. To minimize the risk, it can be applied the certain data mining technique to predict the future behavior of the customers. In this study, it is explored in some data mining techniques such as C4.5 and Naive Bayes for this purpose. The customer attributes used in this study are: salary, age, marital status, other installments and worthiness. The experiments are performed by using the Weka software. Based on evaluation criteria, i.e. accuracy, C4.5 algorithm outperforms compared to Naive Bayes. The percentage split experiment scenarios provide the precision value of 89.16% and the accuracy value of 83.33% wheres the cross validation experiment scenarios give the higher accuracy values of all used k-fold. The C4.5 experiment results also confirm that the most influential instant data attribute in this research is the salary.
A Survey on Heart Disease Prediction Techniquesijtsrd
Heart disease is the main reason for a huge number of deaths in the world over the last few decades and has evolved as the most life threatening disease. The health care industry is found to be rich in information. So, there is a need to discover hidden patterns and trends in them. For this purpose, data mining techniques can be applied to extract the knowledge from the large sets of data. Many researchers, in recent times have been using several machine learning techniques for predicting the heart related diseases as it can predict the disease effectively. Even though a machine learning technique proves to be effective in assisting the decision makers, still there is a scope for developing an accurate and efficient system to diagnose and predict the heart diseases thereby helping doctors with ease of work. This paper presents a survey of various techniques used for predicting heart disease and reviews their performance. G. Niranjana | Dr I. Elizabeth Shanthi "A Survey on Heart Disease Prediction Techniques" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-2 , February 2021, URL: https://www.ijtsrd.com/papers/ijtsrd38349.pdf Paper Url: https://www.ijtsrd.com/computer-science/other/38349/a-survey-on-heart-disease-prediction-techniques/g-niranjana
PREDICTIVE ANALYTICS IN HEALTHCARE SYSTEM USING DATA MINING TECHNIQUEScscpconf
The health sector has witnessed a great evolution following the development of new computer technologies, and that pushed this area to produce more medical data, which gave birth to multiple fields of research. Many efforts are done to cope with the explosion of medical data on one hand, and to obtain useful knowledge from it on the other hand. This prompted researchers to apply all the technical innovations like big data analytics, predictive analytics, machine learning and learning algorithms in order to extract useful knowledge and help in making decisions. With the promises of predictive analytics in big data, and the use of machine learning
algorithms, predicting future is no longer a difficult task, especially for medicine because predicting diseases and anticipating the cure became possible. In this paper we will present an overview on the evolution of big data in healthcare system, and we will apply a learning algorithm on a set of medical data. The objective is to predict chronic kidney diseases by using Decision Tree (C4.5) algorithm.
Framework for comprehensive enhancement of brain tumor images with single-win...IJECEIAES
Usage of grayscale format of radiological images is proportionately more as compared to that of colored one. This format of medical image suffers from all the possibility of improper clinical inference which will lead to error-prone analysis in further usage of such images in disease detection or classification. Therefore, we present a framework that offers single-window operation with a set of image enhancing algorithm meant for further optimizing the visuality of medical images. The framework performs preliminary pre-processing operation followed by implication of linear and non-linear filter and multi-level image enhancement processes. The significant contribution of this study is that it offers a comprehensive mechanism to implement the various enhancement schemes in highly discrete way that offers potential flexibility to physical in order to draw clinical conclusion about the disease being monitored. The proposed system takes the case study of brain tumor to implement to testify the framework.
PERFORMANCE OF DATA MINING TECHNIQUES TO PREDICT IN HEALTHCARE CASE STUDY: CH...ijdms
This document discusses applying machine learning algorithms to predict chronic kidney disease. It:
1) Applied three algorithms (C4.5 decision tree, SVM, and Bayesian Network) to a chronic kidney disease dataset containing 400 patients and 24 attributes to classify patients as having chronic kidney disease or not.
2) Found that the C4.5 decision tree algorithm had the best performance based on accuracy (63%), error rate (0.37), kappa statistic (0.97), and other evaluation metrics. SVM and Bayesian Network performance was lower.
3) Concludes C4.5 decision tree is the most efficient algorithm for predicting chronic kidney disease based on this medical dataset.
The Analysis of Performace Model Tiered Artificial Neural Network for Assessm...IJECEIAES
The assessment model of coronary heart disease is so much developed in line with the development of information technology, particularly the field of artificial intelligence. Unfortunately, the assessment models developed mostly do not use such an approach made by the clinician, that is the tiered approach. This makes the assessment process should conduct a thorough examination. This study aims to analyze the performance of a tiered model assessment. The assessment system is divided into several levels, with reference to the stages of the inspection procedure.The method used for each level is, preprocessing, building architecture artificial neural network (ANN), conduct training using the Levenberg-Marquardt algorithm and one step secant, as well as testing the system. The test results showed the influence of each level, both when the output level of the previous positive or negative, were tested back at the next level. The effect indicates that the level above gives performance improvement and or strengthens the performance at the previous level.
Existing model uses structured data to predict the patients of either high risk or low risk.
But for a complex disease, structured data is not a good way to describe the disease.
We propose a new convolutional neural network (CNN)-based multimodal disease risk prediction algorithm using structured and unstructured data from hospital.
In this paper, we mainly focus on the risk prediction of cerebral infarction.
IRJET - Classification and Prediction for Hospital Admissions through Emergen...IRJET Journal
This document discusses using machine learning techniques to predict hospital admissions from emergency departments in order to improve patient flow and reduce overcrowding. It compares the performance of logistic regression and random forest algorithms on a dataset. Logistic regression identified several factors related to admissions including age, arrival mode, previous admissions. Random forests had the lowest accuracy. Predictive models could allow advance planning of resources to prevent bottlenecks. Future work involves exploring additional machine learning methods.
Survey on data mining techniques in heart disease predictionSivagowry Shathesh
This document summarizes research on using data mining techniques to predict heart disease. It discusses previous work using classification, clustering, association rule mining and other techniques on several heart disease datasets. Classification algorithms like naive bayes, decision trees and neural networks have been widely used with naive bayes found to often provide the best performance. Feature selection and attribute reduction are also examined. The document provides an overview of the key steps and techniques in medical data mining and predictive analysis for heart disease.
IRJET- Chronic Kidney Disease Prediction based on Naive Bayes TechniqueIRJET Journal
This document discusses using a Naive Bayes technique to predict chronic kidney disease (CKD) based on patient data. It begins by introducing data mining and its applications in healthcare to extract useful information from large datasets. It then reviews literature on using classification algorithms like Naive Bayes for disease detection. Next, it describes the limitations of existing manual CKD prediction systems. The proposed system would automate CKD prediction using a Naive Bayes classifier to help doctors diagnose the disease which affects many worldwide. The methodology involves collecting clinical data, pre-processing it, then applying the Naive Bayes technique to extract patterns and predict CKD.
Hybrid Technique for Associative Classification of Heart DiseasesJagdeep Singh Malhi
The document discusses a hybrid technique for associative classification of heart diseases using data mining. It summarizes existing classification and association rule mining algorithms applied to heart disease data. The author aims to improve accuracy by generating classification association rules efficiently and integrating classification with association rule mining. The proposed approach is implemented in Weka to extract rules from a heart disease dataset using Apriori and FP-Growth algorithms. The rules are used to classify patients and evaluate the performance compared to other methods.
Deep learning in healthcare: Oppotunities and challenges with Electronic Medi...Thien Q. Tran
Interested in deep learning for healthcare has grown strongly recent years besides with the successes in other domains such as Computer Vision, Natural Language Processing, Speech Recognition and so forth. This talk will try to give a brief look into the recent effort of research in deep learning for healthcare. Especially, this talk focuses on the opportunities and challenges in using electronic health records (EHR) data, which is one of the most important data sources in healthcare domain.
Heart Disease Prediction Using Data Mining TechniquesIJRES Journal
There are huge amounts of data in the medical industry which is not processed properly and hence cannot be used effectively in making decisions. We can use data mining techniques to mine these patterns and relationships. This research has developed a prototype Heart Disease Prediction using data mining techniques, namely Neural Network, K-Means Clustering and Frequent Item Set Generation. Using medical profiles such as age, sex, blood pressure and blood sugar it can predict the likelihood patients getting a heart disease. It enables significant knowledge, e.g. patterns, relationships between medical factors related to heart disease to be established. Performance of these techniques is compared through sensitivity, specificity and accuracy. It has been observed that Artificial Neural Networks outperform K Means clustering in all the parameters i.e. Sensitivity, Specificity and Accuracy.
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
This document summarizes a research paper that proposes a machine learning approach to identify disease-treatment relationships from biomedical text. It extracts sentences mentioning diseases and treatments from medical publications and classifies the semantic relationships between them. The researchers evaluate their methodology on a dataset of sentences annotated with diseases, treatments and their relationships. Their results show the machine learning models can reliably extract this information and outperform previous methods on the same data. The proposed approach could be integrated into applications to disseminate healthcare information from published literature to medical professionals and patients.
Survey on data mining techniques in heart disease predictionSivagowry Shathesh
This document describes a study on applying data mining techniques to analyze and predict heart disease. It discusses how data mining can extract valuable knowledge from healthcare data. The study uses several data mining techniques like decision trees, naive Bayes classification, clustering, and association rule mining on heart disease datasets from UC Irvine to predict heart disease. Experimental results show that multilayer neural networks and classification techniques like naive Bayes had higher prediction accuracy compared to other methods.
Heart Disease Prediction using Data MiningIRJET Journal
This document describes a study that uses data mining techniques like neural networks and genetic algorithms to predict heart disease based on major risk factors. The proposed system initializes neural network weights using a genetic algorithm for feature selection and classification to build an intelligent clinical decision support system. It analyzes heart disease risk factors like age, cholesterol, blood pressure, smoking status and diabetes using a neuro-fuzzy model optimized with a genetic algorithm. The system is able to predict heart disease with 89% accuracy and can help detect the disease early to improve treatment outcomes.
MULTI-CRITERIA DECISION SUPPORT GUIDED BY CASE-BASED REASONINGcsandit
Many systems based on knowledge, especially expert systems for medical decision support have
been developed. Only systems are based on production rules, and cannot learn and evolve only
by updating them. In addition, taking into account several criteria induces an exorbitant number
of rules to be injected into the system. It becomes difficult to translate medical knowledge or a
support decision as a simple rule. Moreover, reasoning based on generic cases became classic
and can even reduce the range of possible solutions. To remedy that, we propose an approach
based on using a multi-criteria decision guided by a case-based reasoning (CBR) approach.
Nilesh Bhaskarrao Bahadure presents information on biomedical image processing and signal analysis. The document discusses biomedical signals, their origin and dynamics, and processing techniques. It explains that physiological processes produce signals that can provide information about health and disease states. Advanced signal processing is needed to extract clinically relevant data from complex biomedical signals. The document also describes computer-aided diagnosis systems, which apply computer technology to medical imaging to assist physicians' clinical decision making and improve diagnostic accuracy.
Early Identification of Diseases Based on Responsible Attribute using Data Mi...IRJET Journal
This document describes a proposed method for early identification of diseases using data mining and classification techniques. It begins with an introduction to classification and discusses how it is commonly used in healthcare for tasks like predicting patient risk levels. It then reviews related literature applying classification methods to diseases like heart disease and diabetes. The document outlines the problem of selecting the best classification technique for a given healthcare dataset. It proposes an architecture and method for disease prediction that assigns recommended values to attributes and classifies unknown data based on calculating totals. The method is experimentally analyzed using a heart disease dataset, and its accuracy is compared to Bayesian classification. In conclusion, the proposed method seeks to reduce attributes and complexity while accurately classifying patient data for early disease identification.
This document discusses using data mining techniques like association rule mining and improved apriori algorithm with fuzzy logic to develop an expert system that can predict the risk of osteoporosis based on a patient's clinical data and history. It aims to help doctors make more informed decisions early on to prevent osteoporosis. The system would find relationships between various risk factors and diagnose osteoporosis severity to identify at-risk patients before costly tests. Literature on using different algorithms like decision trees and neural networks for medical diagnosis and predicting osteoporosis risk is also reviewed.
Drug stability refers to a drug substance or product remaining within established specifications over time. The stability of a product is expressed as its shelf life or expiry period. Stability testing involves multiple stages from early stress testing to ongoing long-term testing as required by regulatory bodies. Stability is affected by various factors related to the drug, formulation, and environment. Reduced stability study designs like bracketing and matrixing allow testing of representative samples and are acceptable with proper scientific justification.
stability tests for pharmaceutical productsalaaalfayez
These documents provide guidance on stability testing and evaluation for pharmaceutical products. The purpose of stability testing is to provide evidence on how a drug product's quality varies over time under various environmental conditions. Key aspects addressed include testing the drug substance and finished product under different timepoints and storage conditions to establish or extend a product's shelf life. The documents outline best practices for conducting long-term, accelerated, and intermediate stability studies to evaluate the impact of factors like temperature, humidity, and light on a product's physical, chemical, biological, and microbiological properties over time.
1. The document discusses the stability of pharmaceutical products and factors that affect stability such as temperature, moisture, light, and packaging.
2. It covers different types of stability including chemical, physical, and microbiological stability. Regulatory requirements for stability studies and guidelines from organizations like ICH are also reviewed.
3. The major pathways for drug degradation are described as physical degradation, chemical degradation through oxidation, hydrolysis, and other reactions. Methods to protect against these degradation pathways are summarized.
The document discusses guidelines for stability testing from the International Conference on Harmonisation (ICH). It provides an overview of several ICH guidelines related to stability testing of drug substances and products, including guidelines on photostability testing, new dosage forms, bracketing and matrixing designs, and evaluation of stability data. It also summarizes key aspects of conducting stability studies such as selecting representative batches, appropriate container closure systems, testing frequency and storage conditions, and evaluation of results. Stress testing is discussed as a way to validate analytical methods and identify potential degradants.
MULTI-CRITERIA DECISION SUPPORT GUIDED BY CASE-BASED REASONINGcscpconf
Many systems based on knowledge, especially expert systems for medical decision support have been developed. Only systems are based on production rules, and cannot learn and evolve only
by updating them. In addition, taking into account several criteria induces an exorbitant number of rules to be injected into the system. It becomes difficult to translate medical knowledge or asupport decision as a simple rule. Moreover, reasoning based on generic cases became classic and can even reduce the range of possible solutions. To remedy that, we propose an approach
based on using a multi-criteria decision guided by a case-based reasoning (CBR) approach.
Josephine Briggs, MD
Director
National Center for Complementary and Alternative Medicine
National Institutes of Health
Opening Keynote "Research in an IT Connected World: Building Better Partnerships – NIH and Health Care Systems"
The era of ‘Big Data’ has arrived for biomedical research, bringing with it immense challenges as well as spectacular opportunities. NIH is establishing major programs with the potential to transform the future of US biomedical research by building the capacities necessary for these challenges. These programs will strengthen research partnerships with health care systems and the IT networks that support them.
The Big Data to Knowledge (BD2K) initiative, to be launched in 2014, will implement a set of recommendations from the Data and Informatics Working Group to the Advisory Committee to the Director. Investments are planned to meet scientific needs to manage and utilize large complex datasets, including strengthening training, and investing in improved analysis methods and software development and dissemination. NIH is also evaluating strengthening data and software sharing policies, and the potential creation of catalogs of research data, and data/metadata standards.
The Common Fund’s Health Care Systems (HCS) Research Collaboratory program has the goal to strengthen the national capacity to implement cost-effective large-scale research studies by engaging major health care delivery organizations as research partners. The aim of the program is to provide a framework of implementation methods and best practices that will enable the participation of many health care systems in clinical research. Research conducted in partnership with health care systems is essential to strengthen the relevance of research results to health practice. Seven demonstration projects, currently in a feasibility phase, are developing detailed methods to implement rigorous randomized studies of questions of major public health impact. These studies, and the IT infrastructure that will make them possible, will be described in detail.
This document proposes a case-based reasoning model (CBRM) to facilitate knowledge sharing and improve medical decision making. The CBRM would automatically model physicians' experiences with patients by collecting data on symptoms, treatments, medications, and outcomes. This data would be analyzed statistically to identify patterns and common factors among similar disease cases. The CBRM uses a four step process (retrieve, reuse, revise, retain) inspired by human reasoning to match new patient cases to previous similar cases and help physicians identify relevant diagnoses and treatments. The goal is to improve patient care by providing physicians quick access to the most relevant past case histories and clinical insights for better decision making.
Application Evaluation Project Part 1 Evaluation Plan FocusTec.docxalfredai53p
Application: Evaluation Project Part 1: Evaluation Plan Focus
Technology increases human effectiveness. Using a lever, you can move an object several times your size. In an airplane, you can move exponentially faster than on foot. Using the Internet, you can access information much more quickly than at a library. What possibilities like this exist in the nursing field? What health information technologies can amplify your impact as a nurse far more than ever before? In this Evaluation Project, you will have the opportunity to answer these questions.
Because of the great differences between HIT systems and different goals of an evaluation, there is no one-size-fits-all evaluation plan. Different technologies require different evaluation methods. Consequently, in this part of your Evaluation Project, you will conduct research on how system implementations similar to the one you select have been previously evaluated. After exploring similar system implementations, you will select one research goal and viewpoint to use in the evaluation.
Read the following three scenarios, and select the one that is of most interest to you:
Scenario 1:
Your hospital is implementing a new unified acute and ambulatory Electronic Health Record (EHR) system through which patient care documentation will occur. Interdisciplinary assessment forms (including nursing), clinical decision support, and medical notes will be documented in this system. The implementation of the system is anticipated to improve the hospital’s performance in a multitude of areas. In particular, it is hoped that the use of the EHR system will reduce the rate of patient safety events, improve the quality of care, deter sentinel events, reduce patient readmissions, and impact spending. The implementation of the EHR system is also intended to fulfill the “Meaningful Use” requirements stipulated in the Health Information Technology for Economic and Clinical Health (HITECH) Act. As the hospital’s lead nurse informaticist, you have been tasked with planning the evaluation of the EHR implementation.
Scenario 2:
As the lead nurse informaticist in your hospital, you have been given the task of planning an evaluation for a soon-to-be launched computerized provider order entry (CPOE) system. The CPOE system is designed to replace conventional methods of placing medication, laboratory, admission, referral, and radiology orders. CPOE systems enable health care providers to electronically specify orders, rather than rely on paper prescriptions, telephone calls, and faxes. The intended goal of a CPOE system is to improve safety by ensuring that orders are easily comprehensible through the use of evidence-based order sets. In addition, the CPOE system has the potential for improving workflow by avoiding duplicate orders and reducing the steps between those who place medical orders and their recipients.
Scenario 3:
You are the lead nurse informaticist in a large urban hospital that has recently implemented a new .
COVID-19 knowledge-based system for diagnosis in Iraq using IoT environmentnooriasukmaningtyas
The importance and benefits of healthcare mobile applications is increasing rapidly, especially when such applications are connected to the internet of things (IoT). This paper describes a smart knowledge-based system (KBS) that helps patients showing symptoms of Influenza verify being infected with Coronavirus, commonly known as COVID-19. In addition to the systems’ diagnostic functionality, it helps these patients get medical assistance fast by notifying medical authorities using the IoT. This system displays patient’s location, phone number, date and time of examination. During the applications’ development, the developers used Twilio, short message service (SMS), WhatsApp, and Google map applications.
System for Recommending Drugs Based on Machine Learning Sentiment Analysis of...IRJET Journal
This document presents a system for recommending drugs based on machine learning sentiment analysis of drug reviews. The system aims to minimize the workload on experts by providing drug recommendations based on patient feedback. It uses various machine learning and deep learning techniques like naive bayes, logistic regression, LSTM, and GRU to analyze drug reviews and predict sentiment. Based on the sentiment analysis of features extracted from reviews, the system conditionally recommends medications for specific patients and conditions. It achieved up to 93% accuracy in recommending the top drugs for common conditions like acne, high blood pressure, anxiety, and depression. The system aims to enhance healthcare access and reduce medical errors by supplementing expert recommendations with an AI-powered recommendation platform.
The document describes a proposed clinical decision support system that uses k-means clustering and an artificial neural network with particle swarm optimization to classify patient data and determine diagnoses. It begins with background on clinical decision making and existing systems. It then outlines the proposed system, which involves clustering patient data using k-means, and training an artificial neural network using particle swarm optimization and backpropagation to classify new patient data and determine optimal treatment. The combination of these techniques is meant to improve accuracy, efficiency, time consumption and costs compared to other methods.
Proposed Model for Chest Disease Prediction using Data Analyticsvivatechijri
Chest diseases if not properly diagnosed in early stages can be fatal. Because of lack of skilled
knowledge or experiences of real life practitioners, many a times one chest disease is wrongly diagnosed for the
other, which leads to wrong treatment. Due to this the actual disease keeps on growing and become fatal. For
example, muscular chest pains can be treated for the heart disease or COPD is treated for Asthma. Early
prediction of chest disease is crucial but is not an easy task. Consequently, the computer based prediction system
for chest disease may play a significant role as a pre-stage detection to take proper actions with a view to recover
from it. However the choice of the proper Data Mining classification method can effectively predict the early
stage of the disease for being cured from it. In this paper, the three mostly used classification techniques such as
support vector machine (SVM), k-nearest neighbour (KNN) and artificial neural network (ANN) have been studied
with a view to evaluating them for chest disease prediction.
The Healthcare industry contains big and complex data that may be required in order to discover fascinating pattern of diseases & makes effective decisions with the help of different machine learning techniques. Advanced data mining techniques are used to discover knowledge in database and for medical research. This paper has analyzed prediction systems for Diabetes, Kidney and Liver disease using more number of input attributes. The data mining classification techniques, namely Support Vector Machine(SVM) and Random Forest (RF) are analyzed on Diabetes, Kidney and Liver disease database. The performance of these techniques is compared, based on precision, recall, accuracy, f_measure as well as time. As a result of study the proposed algorithm is designed using SVM and RF algorithm and the experimental result shows the accuracy of 99.35%, 99.37 and 99.14 on diabetes, kidney and liver disease respectively.
AN ALGORITHM FOR PREDICTIVE DATA MINING APPROACH IN MEDICAL DIAGNOSISijcsit
The document describes a predictive data mining algorithm for medical diagnosis that uses support vector machine (SVM) and random forest (RF) algorithms. It analyzes diabetes, kidney, and liver disease databases using these techniques. The proposed algorithm applies SVM and RF to the datasets and achieves high prediction accuracies of 99.35%, 99.37%, and 99.14% for diabetes, kidney, and liver diseases respectively. It also compares the performance of SVM and RF based on metrics like precision, recall, accuracy, and execution time.
A hybrid model for heart disease prediction using recurrent neural network an...BASMAJUMAASALEHALMOH
This document presents research on developing a hybrid deep learning model using recurrent neural networks (RNN) and long short-term memory (LSTM) to predict heart disease. The researchers created a model that classifies synthetic cardiac data using different RNN and LSTM approaches with cross-validation. They evaluated the system's performance using various machine learning methods and found that the deep hybrid learning approach was more accurate than either classic deep learning or machine learning alone. The document provides background on heart disease and motivation for developing a more accurate predictive model, describes the methodology used including the dataset, and outlines the experimental setup and algorithm.
Chronic Kidney Disease prediction is one of the most important issues in healthcare analytics. The most interesting and challenging tasks in day to day life is prediction in medical field. In this paper, we employ some machine learning techniques for predicting the chronic kidney disease using clinical data. We use three machine learning algorithms such as Decision Tree(DT) algorithm, Naive Bayesian (NB) algorithm. The performance of the above models are compared with each other in order to select the best classifier in predicting the chronic kidney disease for given dataset.
AN IMPROVED MODEL FOR CLINICAL DECISION SUPPORT SYSTEMijaia
The document describes an improved model for a clinical decision support system that was developed to address issues with misdiagnosis and inconsistent healthcare records. The system incorporates both knowledge-based and non-knowledge based decision support methods using a hybrid approach. It was trained and validated using prostate cancer and diabetes datasets, achieving classification accuracies of 98% and 94% respectively. The system aims to enhance disease detection and prediction to support better healthcare delivery.
ICU Patient Deterioration Prediction : A Data-Mining Approachcsandit
A huge amount of medical data is generated every da
y, which presents a challenge in analysing
these data. The obvious solution to this challenge
is to reduce the amount of data without
information loss. Dimension reduction is considered
the most popular approach for reducing
data size and also to reduce noise and redundancies
in data. In this paper, we investigate the
effect of feature selection in improving the predic
tion of patient deterioration in ICUs. We
consider lab tests as features. Thus, choosing a su
bset of features would mean choosing the
most important lab tests to perform. If the number
of tests can be reduced by identifying the
most important tests, then we could also identify t
he redundant tests. By omitting the redundant
tests, observation time could be reduced and early
treatment could be provided to avoid the risk.
Additionally, unnecessary monetary cost would be av
oided. Our approach uses state-of-the-art
feature selection for predicting ICU patient deteri
oration using the medical lab results. We
apply our technique on the publicly available MIMIC
-II database and show the effectiveness of
the feature selection. We also provide a detailed a
nalysis of the best features identified by our
approach.
ICU PATIENT DETERIORATION PREDICTION: A DATA-MINING APPROACHcscpconf
A huge amount of medical data is generated every day, which presents a challenge in analysing
these data. The obvious solution to this challenge is to reduce the amount of data without
information loss. Dimension reduction is considered the most popular approach for reducing
data size and also to reduce noise and redundancies in data. In this paper, we investigate the
effect of feature selection in improving the prediction of patient deterioration in ICUs. We
consider lab tests as features. Thus, choosing a subset of features would mean choosing the
most important lab tests to perform. If the number of tests can be reduced by identifying the
most important tests, then we could also identify the redundant tests. By omitting the redundant
tests, observation time could be reduced and early treatment could be provided to avoid the risk.
Additionally, unnecessary monetary cost would be avoided. Our approach uses state-of-the-art
feature selection for predicting ICU patient deterioration using the medical lab results. We
apply our technique on the publicly available MIMIC-II database and show the effectiveness of
the feature selection. We also provide a detailed analysis of the best features identified by our
approach.
In this research work we have developed a strategy in which the various parameters that influence the occurrence of pulmonary disease have been gathered from survey of doctors who specialize in diagnoses of pulmonary disease and diagnostic recipes involving if the else rules were built and given labels, which were used as target for machine learning algorithms [Logistic , SVM, RBF, Naïve Bayes ] for identification of input dataset of symptoms of subjects . Multiple designs of these classifiers were implemented and best possible machine algorithm was identified for implementing the complete methodology. Results shows that there was no absolute answer for the design and selection of best possible machine algorithm as evident from the results based on multiple statistical tests, therefore , distance from ideal values of statistical test to find best classifier with most optimized parameters was calculated and the classifier which had closest to these ideal values was found and declared the best classifier for identification of pulmonary diseases presence or absence .as per results naïve bayes classifier is performing best which is evident from the statistical test scores .
Case Retrieval using Bhattacharya Coefficient with Particle Swarm Optimizationrahulmonikasharma
Now a day, health information management and utilization is the demanding task to health informaticians for delivering the eminence healthcare services. Extracting the similar cases from the case database can aid the doctors to recognize the same kind of patients and their treatment details. Accordingly, this paper introduces the method called H-BCF for retrieving the similar cases from the case database. Initially, the patient’s case database is constructed with details of different patients and their treatment details. If the new patient comes for treatment, then the doctor collects the information about that patient and sends the query to the H-BCF. The H-BCF system matches the input query with the patient’s case database and retrieves the similar cases. Here, the PSO algorithm is used with the BCF for retrieving the most similar cases from the patient’s case database. Finally, the Doctor gives treatment to the new patient based on the retrieved cases. The performance of the proposed method is analyzed with the existing methods, such as PESM, FBSO-neural network, and Hybrid model for the performance measures accuracy and F-Measure. The experimental results show that the proposed method attains the higher accuracy of 99.5% and the maximum F-Measure of 99% when compared to the existing methods.
T OP K-O PINION D ECISIONS R ETRIEVAL IN H EALTHCARE S YSTEM csandit
The aim of this paper is to use data mining techniq
ue and opinion mining(OM) concepts to the
field of health informatics. The decision making in
health informatics involves number of
opinions given by the group of medical experts for
specific disease in the form of decision based
opinions which will be presented in medical databas
e in the form of text. These decision based
opinions are then mined from database with the help
of mining technique. Text document
clustering plays major role in the fast developing
information Explosion. It is considered as tool
for performing information based operations. Text d
ocument clustering generates clusters from
whole document collection automatically, normally K
-means clustering technique used for text
document clustering. In this paper we use Bisecting
K-means clustering technique and it is
better compared to traditional K-means technique. T
he objective is to study the revealed
groupings of similar opinion-types associated with
the likelihood of physicians and medical
experts.
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.
Heart Disease Prediction Using Data MiningIRJET Journal
This document discusses using data mining techniques like Naive Bayes and Weighted Associative Classifier (WAC) to predict heart disease. It analyzes a dataset containing factors like age, sex, medical history, and test results. Naive Bayes and WAC are used to generate rules for predicting whether a patient has heart disease risk. The system was able to indicate heart disease risk levels based on the patient's data. The document concludes the approach was effective for heart disease prediction and automation could further improve clinical decision making.
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Case Based Medical Diagnosis of Occupational Chronic Lung Diseases From Their Symptoms and Signs
1. Prem Pal Singh Tomar, Dr. Ranjit Singh, Dr. P K Saxena & Jeetu Sharma
International Journal of Biometrics and Bioinformatics (IJBB), Volume (5) : Issue (4) : 2011 216
Case Based Medical Diagnosis of Occupational Chronic Lung
Diseases From Their Symptoms and Signs
Prem Pal Singh Tomar singhppst@rediffmail.com
Faculty of Engineering,
Dayalbagh Educational Institute,
Uttar Pradesh,
Agra, India
Dr. Ranjit Singh rsingh_dei@yahoo.com
Faculty of Engineering,
Dayalbagh Educational Institute,
Uttar Pradesh,
Agra, India
Dr. P K Saxena premkumarsaxena@gmail.com
Faculty of Engineering,
Dayalbagh Educational Institute,
Uttar Pradesh,
Agra, India
Jeetu Sharma jitusharma_19@rediffmail.com
Electronics & Electrical engineering
Anand Engg. College
Agra
Abstract
The clinical decision support system using the case based reasoning (CBR) methodology of Artificial
Intelligence (AI) presents a foundation for a new technology of building intelligent computer aided
diagnoses systems. This Technology directly addresses the problems found in the traditional Artificial
Intelligence (AI) techniques, e.g. the problems of knowledge acquisition, remembering, robust and
maintenance. In this paper, we have used the Case Based Reasoning methodology to develop a clinical
decision support system prototype for supporting diagnosis of occupational lung diseases. 127 cases
were collected for 14 occupational chronic lung diseases, which contains 26 symptoms. After removing
the duplicated cases from the database, the system has trained set of 47 cases for Indian Lung patients.
Statistical analysis has been done to determine the importance values of the case features. The retrieval
strategy using nearest-neighbor approaches is investigated. The results indicate that the nearest
neighbor approach has shown the encouraging outcome, used as retrieval strategy. A Consultant
Pathologist’s interpretation was used to evaluate the system. Results for Sensitivity, Specificity, Positive
Prediction Value and the Negative Prediction Value are 95.3%, 92.7%, 98.6% and 81.2% respectively.
Thus, the result showed that the system is capable of assisting an inexperience pathologist in making
accurate, consistent and timely diagnoses, also in the study of diagnostic protocol, education, self-
assessment, and quality control. In this paper, clinical decision support system prototype is developed
for supporting diagnosis of occupational lung diseases from their symptoms and signs through
employing Microsoft Visual Basic .NET 2005 along with Microsoft SQL server 2005 environment with
the advantage of Object Oriented Programming technology
Key words: Clinical Support System, Artificial Intelligence, Case-Based Reasoning, Pathologist
2. Prem Pal Singh Tomar, Dr. Ranjit Singh, Dr. P K Saxena & Jeetu Sharma
International Journal of Biometrics and Bioinformatics (IJBB), Volume (5) : Issue (4) : 2011 217
1. INTRODUCTION
The use of artificial Intelligence (AI) technique i.e case-based reasoning (CBR), in the the development
of Clinical Support System has a relatively young history, arose out of the research in cognitive science.
The earliest contributions in this area were from Roger Schank and his colleagues at Yale University
[1],[2]. During the period 1977–1993, CBR research was highly regarded as a plausible high-level model
for cognitive processing. It was focused on problems such as how people learn a new skill and how
humans generate hypotheses about new situations e cognitive-based researches were to construct
decision based on their past experiences. Many prototype of decision support system based on CBR
technique were built during this period: for example, Cyrus [3],[4], Mediator [5], Persuader [6], Chef [7],
Julia [8], Casey, and Protos [9]. Three CBR workshops were organized in 1988, 1989, and 1991 by the
U.S. Defense Advanced Research Projects Agency (DARPA). These formally marked the birth of the
discipline of Decision Support System using case-based reasoning.
Computerized evidence-based guidelines and Clinical decision support systems (CDSS) have been
promoted as the key to improving effectiveness and efficiency of clinical decisions [14]. Although the use
of decision support systems (DSS) in the field of medicine has accelerated in recent years [15].
Many researchers are working on Clinical Support System using CBR with many diverse
applications ranging from psychiatry and epidemiology to clinical diagnosis Most of them aim for a
successful implementation of CBR methods to enhance the work of health experts to improve the
efficiency and quality of health care Researchers who have contributed substantially to CBR in medicine
include Gierl Schmidt and their colleagues who focused on a range of applications including children
dysmorphic syndromes, antibiotics therapy advising for intensive care and monitoring emerging diseases
(Gierl, 1993 Schmidt & Gierl, 2001) Notable is their ICONS system Gierl, 1993 ),first applied to the
determination of antibiotic therapy treatment for intensive care then to the prognosis of kidney function
defects, For this latter application ICONS learned prototypes associated with graded levels of severity
through temporal abstraction Gierl, 1993),and matched new cases with these prototypes to predict the
severity of a renal disease [16].
Some real Clinical Support Systems based on CBR technique are: CASEY that gives a diagnosis for the
heart disorders [10], GS.52 which is a diagnostic support system for dysmorphic syndromes, NIMON is a
renal function monitoring system, COSYL that gives a consultation for a liver transplanted patient [11]
and ICONS that presents a suitable calculated antibiotics therapy advise for intensive care patients [12].
Computerized evidence-based guidelines and Clinical decision support systems (CDSS) have been
promoted as the key to improving effectiveness and efficiency of clinical decisions [17]. Although the use
of decision support systems (DSS) in the field of medicine has accelerated in recent years [18].
However, none of the aforementioned studies presented results that showed evidence of first, the
inclusion of all the 14 occupational lung diseases perspective; and secondly a system capable of
assisting a Pathologist who is not specialized in the aspect of occupational lung diseases diagnosis.
Thus, this system proposes a medical decision support system for diagnosis of occupational lung
diseases as an improvement of earlier works.
2. JUSTIFICATION FOR STUDY
In clinical practice, making decision involves a careful analysis of harms and benefits associated with
different treatment options. These decisions, often associated with high stake and important long term
consequences, are frequently made in presence of limited resources and information and an incomplete
clinical picture. Under such circumstances, a rigorous and objective analysis of outcomes and
probabilities is essential to achieve the best possible decision given a specific clinical situation.
Therefore, Pathologist is required to be fully conversant with the diversity of possible patterns, recognize
and diagnose them, timely and accurately. Hence, a Pathologist who is not a specialist in the pathology
of the occupational lung diseases has to refer to textbooks and study past diagnosis before concrete
diagnosis can be made and conclusion reached. Hence, there is the need for a system, which can assist
the Pathologist to reach timely and accurate decision.
3. Prem Pal Singh Tomar, Dr. Ranjit Singh, Dr. P K Saxena & Jeetu Sharma
International Journal of Biometrics and Bioinformatics (IJBB), Volume (5) : Issue (4) : 2011 218
3. KNOWLEDGE ENGINEERING TASKS IN DEVELOPING CBR BASED SYSTEM
The problem-solving life cycle in a CBR system consists essentially of the following :
FIGURE 1: Case Base Reasoning Technique
The figure shows that when a new case comes to the system, the system carries out the work of
matching. Upon getting exact match same result is displayed while in case no exact match is found the
nearest neighbor is looked for whose result is adjusted according to the new case using the adaptation
rules and the result is displayed. Such a new case is also saved in the case base for future assistance.
Accordingly the methodology of developing Clinical Support System, CBR-based systems in specific
domain can be summarized in the following steps:
1. Retrieving :The system will search its Case-Memory for an existing case that matches the input
problem specification.
2. Reusing :If we are lucky (our luck increases as we add new cases to the system), we will find a case
that
Exactly matches the input problem and goes directly to a solution.
3. If we are not lucky, we will retrieve a case that is similar to our input situation but not entirely
appropriate to provide as a completed solution.
4. Revising: The system must find and modify small portions of the retrieved case that do not meet the
input specification. This process is called "case-adaptation".
5. Retaining :The result of case adaptation process is (a) Completed solution, and (b) generates a new
case that can be automatically added to the system's case- memory for future use
4. KNOWLEDGE ACQUISITION
Knowledge acquisition is a process of acquiring, organizing and studying knowledge for the lung
diseases. The data and knowledge of Clinical Support System based on Case-Base technique are
collected from different sources. The first primary source is, acquired from a physician (Domain
Expert). The second source is from specialized databases like lung disease diagnostic laboratory at
Agra, Uttar Pradesh, India, books and a few electronic websites. This knowledge can be divided by
important fact into 26 facts, which are shown on table 1.
New Case
Nearest
Neighbor
(Adaptation
rules)
Matching
Solution
Case Base
No Match
New Case
4. Prem Pal Singh Tomar, Dr. Ranjit Singh, Dr. P K Saxena & Jeetu Sharma
International Journal of Biometrics and Bioinformatics (IJBB), Volume (5) : Issue (4) : 2011 219
No. Chief
symptoms
No. Chief
symptoms
1. Cough 14 Alcohol use
2. Dyspnea 15 Heart
rhythm
problem
3. Chest
Discomfort
16 Abdominal
pain
4. Malaise 17 Shoulder
pain
5. Fever 18 difficulty in
swallowing
6. Wheezing 19 Pain under
rib cage
7. Hemoptysis 20 Chemicals
exposure
8. Persistent
cough
21 Fungi
exposure
9. Fever with
chill
22 humidifiers
exposure
10 Night sweat 23 Coke oven
emissions
11. Asbestosis
exposure
24 Silica
exposure
12. Excessive
sweating
25 Coal dust
13. Smoking 26 Cotton Dust
TABLE 1: Fact List of Symptoms
5. ASSIGNING IMPORTANCE VALUE TO CASE SYMPTOMS
Features weights for most problem domains are context dependent. The weight assigned to each
feature of the case tells how much attention to pay to matches and mismatches in the field when
computing the distance measure of a case. Those that are good predictors are then assigned higher
importance for matching [10].
The importance of the feature depends upon its prevalence among the diseases. If a feature is common
among all diseases like Cough, then it will have the least importance in leading to a diagnosis.
6. CASE INDEXING AND RETRIEVAL
Here, we focus our discussion on case indexing and retrieval strategy. Case indexing and retrieval are
two separate but closely related processes. Since a case memory may contain thousands of cases, case
indices organize their key features to expedite the search process. Case retrieval searches the case
base to find candidate cases that share significant features with the new case. Existing literature in case-
based reasoning has proposed several mechanisms for case indexing and retrieval. A good review of
early literature can be found in [13].
7. RETRIEVAL USING NEAREST-NEIGHBOR TECHNIQUE
If however an exact match is not found, which can be the case many times, nearest neighbor technique
(ref. table 2) and adaptation rules have to be used. Let T is new case and C1 and C2 are old cases then
Nearest neighbor formula = sum of (weight*similarity)/sum of weight
(T, C1)=72/97=0.74
(T, C2)=65/97=0.67
So, C1 is the nearest neighbor.
5. Prem Pal Singh Tomar, Dr. Ranjit Singh, Dr. P K Saxena & Jeetu Sharma
International Journal of Biometrics and Bioinformatics (IJBB), Volume (5) : Issue (4) : 2011 220
Then the presence of the symptoms in the new and the old case is listed in the next two columns. Local
similarity is given in Clinical Decision support system. The total of all the weights is calculated by adding
them which is 97. Then the
sum of weight*similarity is calculated by adding all the products of weight*similarity. In the first
comparison the sum is 72 while in the second comparison it is 65. The sum of weights in the first
comparison is: 97
The nearest neighbor value is: 72/97= 0.74
In the second comparison:
The sum of weights is: 65
The sum of weights in the first comparison is: 67
The nearest neighbor value is: 38/67 = 0.57
Therefore, the first comparison, which is case C1, is the nearest neighbor for the new case T.
The system will use the result of the nearest match found and use adaptation rules to ‘revise’
this result according to the demands of the novel situation. The system uses the Nearest-neighbor
algorithm that finds the closest matches of the cases already stored in the database to the new case
using a distance calculation, which determines how similar two cases are by comparing their features,
the pseudo code of this algorithm [10] can be written as follows:
For each feature in the input case:
Find the corresponding feature in the stored case
Compare the two values to each other and compute the degree of match
Multiply by a coefficient representing the importance of the feature to the match
Add the results to derive an average match score
This number represents the degree of match of the old case to the input.
A case can be chosen by choosing the item with the largest score.
Nearest-neighbor techniques applied to the retrieval phase of a CBR system (i.e., measuring similarity
among cases). The equation
Similarity (T, S) =Σ f (Ti Si ) × Wi / Σ Wi
represents a typical nearest-neighbor technique that describes a situation for which T( Target case) and
S ( Source case) are two cases compared for similarity, n is the number of attributes in each case, i is an
individual attribute from 1 to n, and Wi is the feature weight of attribute. Similarities are usually
normalized to fall within the range 0 to 1, where 1 means a perfect match and 0 indicates a total
mismatch.
8. DEVELOPMENT AND RESULTS
Development of clinical decision support system prototype is through employing Microsoft Visual Basic
.NET 2005 environment with the advantage of Object Oriented Programming technology. The Microsoft
SQL server 2005 was used to develop the database module.
6. Prem Pal Singh Tomar, Dr. Ranjit Singh, Dr. P K Saxena & Jeetu Sharma
International Journal of Biometrics and Bioinformatics (IJBB), Volume (5) : Issue (4) : 2011 221
FIGURE 2: Diagnostic window
FIGURE 3 : Code window
FIGURE 4: SQL server window
7. Prem Pal Singh Tomar, Dr. Ranjit Singh, Dr. P K Saxena & Jeetu Sharma
International Journal of Biometrics and Bioinformatics (IJBB), Volume (5) : Issue (4) : 2011 222
In this paper, the architecture, and the implementation of a prototype of a Clinical Support System using
case-based technique that supports diagnosis of occupational lung diseases was developed. Knowledge
structure was represented via a formalism of cases. The system used nearest-neighbor techniques
technique for the retrieval process.
Using a Consultant Pathologist’s interpretation as a “gold standard” (reference test), the system’s
parameters for diagnosing occupational lung diseases were calculated.
(i) True positive (TP):
The diagnostic system yields positive test result for the sample and thus the sample actually has the
disease;
(ii) False positive (FP):
The diagnostic system yields positive test result for the sample but the sample does not actually have
the disease;
(iii) True negative (TN):
The diagnostic system yields negative test result for the sample and the sample does not actually have
the disease; and
(iv) False negative (FN):
The diagnostic system yields negative test result for the sample but the sample actually has the disease.
The formulas for used for calculating Sensitivity,
Specificity, PPV and NPV are:
Sensitivity = [TP/(TP+FN)] x 100% ……… (1)
Specificity = [TN/TN+FP] x 100%..………. (2)
PPV = [TP/ (TP+FP)] x 100% …………… (3)
NPV = [TN/ (TN+FN)] x100%…………..…(4)
Using equations (1), (2), (3) and (4), respectively, the Sensitivity, Specificity, Positive
Prediction Value (PPV) and the Negative Prediction Value of the system are:
Sensitivity = 95.3%;
Specificity = 92.7%;
PPV = 98.6%
NPV = 81.2%.
8. SUMMARY AND CONCLUSION
In this paper we presented a clinical support system, which could be used by stakeholders for arriving at
very vital decisions regarding the diagnosis of 14 occupational chronic lung diseases. The focus was on
the development of a clinical support system that can assist Pathologist, especially those who may not
be specialist in the area of occupational chronic lung diseases treatment. Thus, the system attempts to
improve the effectiveness of diagnosis (in relation to accuracy, timeliness and quality) that is performed
by a human pathologist, rather than improve their efficiency with respect to decision making. Therefore,
the diagnoses made by the system are at least as good as those made by a human expert.
From the development and analysis of Clinical Support System, it is evident that CBR technique of
Artificial Intelligence (AI) is appropriate methodology for all medical domains and tasks for the following
reasons: cognitive adequateness, explicit experience and subjective knowledge, automatic acquisition of
subjective knowledge, and system integration. CBR technique presents an essential technology of
building intelligent Clinical Support System for medical diagnoses that can aid significantly in improving
the decision making of the physicians. the
proposed method gives an Sensitivity = 95.3%; and PPV = 98.6% which is better than the existing
methods. Future research involves more intensive testing using a larger occupational chronic lung
disease database to get more accurate results.
8. Prem Pal Singh Tomar, Dr. Ranjit Singh, Dr. P K Saxena & Jeetu Sharma
International Journal of Biometrics and Bioinformatics (IJBB), Volume (5) : Issue (4) : 2011 223
The use of Microsoft Visual Basic .NET 2005 along with Microsoft SQL server 2005 as database is found
to be very effective in producing the system under windows environment. For future work, more cases
will be added to the case memory.
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