Linkage Detection of Features that Cause Stroke using Feyn Qlattice Machine L...PurwonoPurwono4
Stroke is a disease caused by brain tissue damage because of blockage in the
cerebrovascular system that disrupts body sensory and motoric systems
Stroke disease is one of the highest death cause in the world. Data collection
from Electronic Health Records (EHR) is increasing and has been included
in the health service big data. It can be processed and analyzed using machine
learning to determine the risk group of stroke disease. Machine learning can
be used as a predictor of stroke causes, while the predictor clarifies the
influence of each cause factor of the disease. Our contribution in this research
is to evaluate Feyn Qlattice machine learning models to detect the influence
of stroke disease's main cause features. We attempt to obtain a correlation
between features of the stroke disease, especially on the gender as a feature,
whether any other features can influence the gender feature. This research
utilizes 4908 data of the disease predictor using the Feyn Qlattice model. The
result implies that gender highly impacts age and hypertension on stroke
disease causes. Autorun in Feyn Qlattice model was run with ten epochs,
resulting in 17596 test models at 57s. Query string parameter that was focused
on age and hypertension features resulted in 1245 models at 4s. An increase
of accuracy was found in training metrics from 0.723 to 0.732 and in testing
metrics from 0.695 to 0.708. Evaluation results showed that the model is
reasonably good as a predictor of stroke disease, indicated with blue lines of
AUC in training and testing metrics close to ROC's left side peak curve.
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
The document describes a proposed method for designing a classifier to detect diabetes using neural networks and the fuzzy k-nearest neighbor algorithm. The method would train a neural network using the fuzzy k-NN algorithm on a server and use it to classify diabetes on a mobile device for convenience. Analysis in WEKA showed the method achieved around 72-74% accuracy on 10-fold cross validation of a diabetes dataset with attributes removed. The proposed method is expected to perform comparably to support vector machines with less complexity.
Improve The Performance of K-means by using Genetic Algorithm for Classificat...IJECEIAES
This document summarizes a research paper that used genetic algorithms to improve the performance of the K-means clustering algorithm for classifying heart attack cases. The paper first classified a dataset of 270 heart disease cases using only K-means, which achieved an accuracy of 68.1481%. It then proposed a two-stage method: 1) Using genetic algorithm to select important predictive features for classification. 2) Applying K-means clustering using only the selected features. This improved approach increased the classification accuracy to 84.0741%. The paper concluded that genetic algorithm effectively reduced irrelevant features and strengthened the performance of K-means classification for the heart disease dataset.
Disease prediction in big data healthcare using extended convolutional neural...IJAAS Team
Diabetes Mellitus is one of the growing fatal diseases all over the world. It leads to complications that include heart disease, stroke, and nerve disease, kidney damage. So, Medical Professionals want a reliable prediction system to diagnose Diabetes. To predict the diabetes at earlier stage, different machine learning techniques are useful for examining the data from different sources and valuable knowledge is synopsized. So, mining the diabetes data in an efficient way is a crucial concern. In this project, a medical dataset has been accomplished to predict the diabetes. The R-Studio and Pypark software was employed as a statistical computing tool for diagnosing diabetes. The PIMA Indian database was acquired from UCI repository will be used for analysis. The dataset was studied and analyzed to build an effective model that predicts and diagnoses the diabetes disease earlier.
IRJET- Diabetes Prediction by Machine Learning over Big Data from Healthc...IRJET Journal
This document discusses using machine learning techniques to predict diabetes based on healthcare data. It proposes using preprocessing, K-means clustering, and support vector machine (SVM) classification. Preprocessing cleans and structures the data. K-means clusters the data into groups. SVM classification then predicts whether patients are diabetic or non-diabetic, aiming for a prediction accuracy of 94.9%. The techniques aim to allow for early diabetes prediction using a combination of machine learning methods on both structured and unstructured healthcare data.
A comparative study of cn2 rule and svm algorithmAlexander Decker
This document discusses using data mining techniques like decision trees, CN2 rule, SOM, and K-means clustering to predict heart disease. It provides background on heart disease prevalence and risk factors. The methodology section describes how classification trees, CN2 rule induction, self-organizing maps (SOM), and K-means clustering algorithms work and a comparative study is performed on heart disease data to evaluate the accuracy of each technique. Experimental results show CN2 rule and SOM achieved the highest classification accuracy rates above 93%.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Prediction of Heart Disease using Machine Learning Algorithms: A Surveyrahulmonikasharma
According to recent survey by WHO organisation 17.5 million people dead each year. It will increase to 75 million in the year 2030[1].Medical professionals working in the field of heart disease have their own limitation, they can predict chance of heart attack up to 67% accuracy[2], with the current epidemic scenario doctors need a support system for more accurate prediction of heart disease. Machine learning algorithm and deep learning opens new door opportunities for precise predication of heart attack. Paper provideslot information about state of art methods in Machine learning and deep learning. An analytical comparison has been provided to help new researches’ working in this field.
Linkage Detection of Features that Cause Stroke using Feyn Qlattice Machine L...PurwonoPurwono4
Stroke is a disease caused by brain tissue damage because of blockage in the
cerebrovascular system that disrupts body sensory and motoric systems
Stroke disease is one of the highest death cause in the world. Data collection
from Electronic Health Records (EHR) is increasing and has been included
in the health service big data. It can be processed and analyzed using machine
learning to determine the risk group of stroke disease. Machine learning can
be used as a predictor of stroke causes, while the predictor clarifies the
influence of each cause factor of the disease. Our contribution in this research
is to evaluate Feyn Qlattice machine learning models to detect the influence
of stroke disease's main cause features. We attempt to obtain a correlation
between features of the stroke disease, especially on the gender as a feature,
whether any other features can influence the gender feature. This research
utilizes 4908 data of the disease predictor using the Feyn Qlattice model. The
result implies that gender highly impacts age and hypertension on stroke
disease causes. Autorun in Feyn Qlattice model was run with ten epochs,
resulting in 17596 test models at 57s. Query string parameter that was focused
on age and hypertension features resulted in 1245 models at 4s. An increase
of accuracy was found in training metrics from 0.723 to 0.732 and in testing
metrics from 0.695 to 0.708. Evaluation results showed that the model is
reasonably good as a predictor of stroke disease, indicated with blue lines of
AUC in training and testing metrics close to ROC's left side peak curve.
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
The document describes a proposed method for designing a classifier to detect diabetes using neural networks and the fuzzy k-nearest neighbor algorithm. The method would train a neural network using the fuzzy k-NN algorithm on a server and use it to classify diabetes on a mobile device for convenience. Analysis in WEKA showed the method achieved around 72-74% accuracy on 10-fold cross validation of a diabetes dataset with attributes removed. The proposed method is expected to perform comparably to support vector machines with less complexity.
Improve The Performance of K-means by using Genetic Algorithm for Classificat...IJECEIAES
This document summarizes a research paper that used genetic algorithms to improve the performance of the K-means clustering algorithm for classifying heart attack cases. The paper first classified a dataset of 270 heart disease cases using only K-means, which achieved an accuracy of 68.1481%. It then proposed a two-stage method: 1) Using genetic algorithm to select important predictive features for classification. 2) Applying K-means clustering using only the selected features. This improved approach increased the classification accuracy to 84.0741%. The paper concluded that genetic algorithm effectively reduced irrelevant features and strengthened the performance of K-means classification for the heart disease dataset.
Disease prediction in big data healthcare using extended convolutional neural...IJAAS Team
Diabetes Mellitus is one of the growing fatal diseases all over the world. It leads to complications that include heart disease, stroke, and nerve disease, kidney damage. So, Medical Professionals want a reliable prediction system to diagnose Diabetes. To predict the diabetes at earlier stage, different machine learning techniques are useful for examining the data from different sources and valuable knowledge is synopsized. So, mining the diabetes data in an efficient way is a crucial concern. In this project, a medical dataset has been accomplished to predict the diabetes. The R-Studio and Pypark software was employed as a statistical computing tool for diagnosing diabetes. The PIMA Indian database was acquired from UCI repository will be used for analysis. The dataset was studied and analyzed to build an effective model that predicts and diagnoses the diabetes disease earlier.
IRJET- Diabetes Prediction by Machine Learning over Big Data from Healthc...IRJET Journal
This document discusses using machine learning techniques to predict diabetes based on healthcare data. It proposes using preprocessing, K-means clustering, and support vector machine (SVM) classification. Preprocessing cleans and structures the data. K-means clusters the data into groups. SVM classification then predicts whether patients are diabetic or non-diabetic, aiming for a prediction accuracy of 94.9%. The techniques aim to allow for early diabetes prediction using a combination of machine learning methods on both structured and unstructured healthcare data.
A comparative study of cn2 rule and svm algorithmAlexander Decker
This document discusses using data mining techniques like decision trees, CN2 rule, SOM, and K-means clustering to predict heart disease. It provides background on heart disease prevalence and risk factors. The methodology section describes how classification trees, CN2 rule induction, self-organizing maps (SOM), and K-means clustering algorithms work and a comparative study is performed on heart disease data to evaluate the accuracy of each technique. Experimental results show CN2 rule and SOM achieved the highest classification accuracy rates above 93%.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Prediction of Heart Disease using Machine Learning Algorithms: A Surveyrahulmonikasharma
According to recent survey by WHO organisation 17.5 million people dead each year. It will increase to 75 million in the year 2030[1].Medical professionals working in the field of heart disease have their own limitation, they can predict chance of heart attack up to 67% accuracy[2], with the current epidemic scenario doctors need a support system for more accurate prediction of heart disease. Machine learning algorithm and deep learning opens new door opportunities for precise predication of heart attack. Paper provideslot information about state of art methods in Machine learning and deep learning. An analytical comparison has been provided to help new researches’ working in this field.
IRJET- Smart Medical Assistant using Wearable DeviceIRJET Journal
This document proposes a smart medical assistant system using a wearable device to monitor health and predict medical emergencies like heart attacks. The system has 5 modules: 1) a hardware module containing sensors to monitor heart rate and temperature, 2) a web application for hospitals, 3) a heart attack prediction module using machine learning, 4) a mobile application for patients and ambulances, and 5) an API module connecting the web and mobile applications. The system aims to detect medical emergencies using sensor data, alert hospitals and contacts, and provide health advice and ambulance tracking to assist users.
IRJET - Comparative Study of Cardiovascular Disease Detection AlgorithmsIRJET Journal
The document compares four algorithms - K-Nearest Neighbors, Support Vector Machine, Decision Tree, and Random Forest - for cardiovascular disease detection using data mining techniques. It summarizes previous studies that have used these algorithms on cardiovascular disease data and evaluated their performance. The document concludes that K-Nearest Neighbors, Support Vector Machine, Decision Tree, or Random Forest algorithms could be used for cardiovascular disease detection, and that the best algorithm depends on the specific dataset and type of disease being diagnosed.
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.
Artificial intelligence (AI) is a fast-growing field and its applications to diabetes, a global pandemic, can reform the approach to diagnosis and management of this chronic condition. Principles of machine learning have been used to build algorithms to support predictive models for the risk of developing diabetes or its consequent complications. Digital therapeutics have proven to be an established intervention for lifestyle therapy in the management of diabetes. Patients are increasingly being empowered for self-management of diabetes, and both patients and health care professionals are benefitting from clinical decision support. AI allows a continuous and burden-free remote monitoring of the patient's symptoms and biomarkers. Further, social media and online communities enhance patient engagement in diabetes care. Technical advances have helped to optimize resource use in diabetes. Together, these intelligent technical reforms have produced better glycemic control with reductions in fasting and postprandial glucose levels, glucose excursions, and glycosylated hemoglobin. AI will introduce a paradigm shift in diabetes care from conventional management strategies to building targeted data-driven precision care.
IRJET - Breast Cancer Prediction using Supervised Machine Learning Algorithms...IRJET Journal
This document describes a study that uses supervised machine learning algorithms to predict breast cancer. Three algorithms - decision tree, logistic regression, and random forest - are applied to preprocessed breast cancer data. The random forest model achieved the best accuracy at 98.6% for predicting whether a tumor was benign or malignant. The study aims to develop an early prediction system for breast cancer using machine learning techniques.
MLTDD : USE OF MACHINE LEARNING TECHNIQUES FOR DIAGNOSIS OF THYROID GLAND DIS...cscpconf
Machine learning algorithms are used to diagnosis for many diseases after very important improvements of classification algorithms as well as having large data sets and high performing computational units. All of these increased the accuracy of these methods. The diagnosis of thyroid gland disorders is one of the application for important classification problem. This study majorly focuses on thyroid gland medical diseases caused by underactive or overactive thyroid glands. The dataset used for the study was taken from UCI repository. Classification of this thyroid disease dataset was a considerable task using decision tree algorithm. The overall
prediction accuracy is 100% for training and in range between 98.7% and 99.8% for testing. In this study, we developed the Machine Learning tool for Thyroid Disease Diagnosis (MLTDD), an Intelligent thyroid gland disease prediction tool in Python, which can effectively help to make the right decision, has been designed using PyDev, which is python IDE for Eclipse.
Heart Disease Identification Method Using Machine Learnin in E-healthcare.SUJIT SHIBAPRASAD MAITY
This document describes a student project that aims to develop a machine learning model for heart disease identification and prediction. It discusses existing heart disease diagnosis techniques, identifies the problem and requirements, outlines the proposed algorithm and methodology using supervised learning classification algorithms like K-Nearest Neighbors and logistic regression. Block diagrams and flow charts illustrate the data preprocessing, model training, and web application development steps to classify patients as having heart disease or not and evaluate model performance. The developed system achieves high accuracy for heart disease prediction.
This document describes a heart disease prediction system that uses machine learning algorithms to analyze patient data and predict the presence and severity of heart disease. The system uses four algorithms - random forest, naive bayes, decision tree, and linear regression - to build predictive models using a dataset of 801 patients with 12 medical attributes. The models are evaluated on their accuracy in both detecting heart disease and classifying its severity from 0 to 4. Random forest achieved the highest accuracy of 95.09% while naive bayes had the lowest at 60.38%. The system provides a way to more accurately diagnose heart disease early using data mining of existing patient records.
ody Information Analysis based Personal Exercise Management System IJECEIAES
Recently, people's interest in health is deepening. So health-related systems are being developed. Existing exercise management systems provided users with exercise related information using PC or smart phone. However, there is a problem that the accuracy of the algorithm for analyzing the user's body information and providing information is low.In this paper, we analyze users' body mass index (BMI) and basal metabolic rate (BMR) and we propose a system that provides the user with necessary information through recommendation algorithm. It informs the user of exercise intensity and momentum, and graphs the exercise history of the user. It also allows the user to refer to the fitness history of other users in the same BMI group. This allows the user to receive more personalized services than the existing exercise management system, thereby enabling efficient exercise.
A major challenge facing healthcare organizations (hospitals, medical centers) is
the provision of quality services at affordable costs. Quality service implies diagnosing
patients correctly and administering treatments that are effective. Poor clinical decisions
can lead to disastrous consequences which are therefore unacceptable. Hospitals must
also minimize the cost of clinical tests. They can achieve these results by employing
appropriate computer-based information and/or decision support systems.
Most hospitals today employ some sort of hospital information systems to manage
their healthcare or patient data.
These systems are designed to support patient billing, inventory management and generation of simple statistics. Some hospitals use decision support systems, but they are largely limited. Clinical decisions are often made based on doctors’ intuition and experience rather than on the knowledge rich data hidden in the database.
This practice leads to unwanted biases, errors and excessive medical costs which affects the quality of service provided to patients.
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.
Disease Prediction And Doctor Appointment systemKOYELMAJUMDAR1
This document outlines a disease prediction and doctor appointment system using machine learning. The objectives are to provide quick medical diagnosis to rural patients and enhance access to medical specialists. Five machine learning algorithms - Decision Tree, Random Forest, Naive Bayes, K-Nearest Neighbors, and Support Vector Machine - are used for disease prediction. The system displays predicted diseases and accuracy scores for each algorithm. Users can then book appointments with specialist doctors for their predicted disease.
One of the major purposes manufacturers incorporate AI or ML in their applications is to ease software computations and to predict precise results. I think compared to any other application, a medical application requires a lot of precise computations and therefore, AI is a perfect solution to enhance performance and productivity. While reading the health-tech news, I came across recent research in this regard, the use of AI in predicting a potential stroke or cardiac arrest. ..
This document provides a software requirements specification for an Attendance Management System being developed for JSS Academy of Technical Education. It includes sections on introduction and purpose, general description of product functions and users, specific requirements including functional and non-functional requirements, and analysis models including sequence diagrams, data flow diagrams, and state transition diagrams. The system will allow for student registration and management of attendance, and provide reports. It is intended to help streamline administrative tasks for the educational institution.
Supervised machine learning based liver disease prediction approach with LASS...journalBEEI
In this contemporary era, the uses of machine learning techniques are increasing rapidly in the field of medical science for detecting various diseases such as liver disease (LD). Around the globe, a large number of people die because of this deadly disease. By diagnosing the disease in a primary stage, early treatment can be helpful to cure the patient. In this research paper, a method is proposed to diagnose the LD using supervised machine learning classification algorithms, namely logistic regression, decision tree, random forest, AdaBoost, KNN, linear discriminant analysis, gradient boosting and support vector machine (SVM). We also deployed a least absolute shrinkage and selection operator (LASSO) feature selection technique on our taken dataset to suggest the most highly correlated attributes of LD. The predictions with 10 fold cross-validation (CV) made by the algorithms are tested in terms of accuracy, sensitivity, precision and f1-score values to forecast the disease. It is observed that the decision tree algorithm has the best performance score where accuracy, precision, sensitivity and f1-score values are 94.295%, 92%, 99% and 96% respectively with the inclusion of LASSO. Furthermore, a comparison with recent studies is shown to prove the significance of the proposed system.
IRJET - Review on Classi?cation and Prediction of Dengue and Malaria Dise...IRJET Journal
1. The document reviews machine learning algorithms for classifying and predicting malaria and dengue diseases based on patient symptoms and blood cell images. It proposes a system using Naive Bayes for classification based on symptoms and Convolutional Neural Network (CNN) for image-based classification of blood cell images.
2. The system architecture takes in patient symptom and image data, uses Naive Bayes to classify based on symptoms, then uses CNN on blood cell images to confirm the disease prediction as malaria or dengue.
3. The proposed system aims to provide fast and accurate prediction of diseases with similar symptoms like malaria and dengue using machine learning algorithms instead of traditional methods for improved diagnosis.
Data Mining Techniques In Computer Aided Cancer DiagnosisDataminingTools Inc
Data mining techniques are used in computer aided cancer diagnosis and detection. They help physicians interpret complex diagnoses, combine information from multiple sources, and provide support for differential diagnosis. Specific techniques like neural networks, decision trees, and cluster detection are used in ALL diagnosis. Data mining can also be applied to detect gastric cancer using single nucleotide polymorphism information. It helps organize healthcare claims data to detect cancer patterns and evaluate treatment efficacy. New applications of data mining and neural networks are also helping detect cancers like breast cancer sooner.
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.
IRJET- Predicting Heart Disease using Machine Learning AlgorithmIRJET Journal
1) The document discusses using machine learning algorithms like Naive Bayes and Decision Trees to predict heart disease using a dataset from Kaggle.
2) It describes preprocessing the dataset, training models, and evaluating accuracy. Decision Trees were found to more accurately predict heart disease than Naive Bayes.
3) The models use 13 attributes like age, sex, cholesterol levels, and examine classification performance to identify individuals at risk of heart disease.
This document discusses using machine learning techniques to predict diabetes. Specifically:
- The authors build several prediction models using machine learning algorithms like logistic regression, KNN, decision trees on a diabetes dataset to classify patients as having diabetes or not.
- They evaluate the performance of the different models using metrics like accuracy, and find that KNN achieved the highest accuracy of 78% on the test data.
- The document also reviews several other studies applying techniques like random forests, support vector machines, convolutional neural networks to the same diabetes prediction task and Pima Indian diabetes dataset.
- The authors conduct their own experiments applying algorithms like logistic regression, KNN, decision trees, random forest, XGBoost to the
FORESTALLING GROWTH RATE IN TYPE II DIABETIC PATIENTS USING DATA MINING AND A...IAEME Publication
The race for urbanization and thirst for high living status leads to unhealthy life.
As the result a rapid growth in number of diabetic patients in urban areas
approaching to its deadline. In this situation it become a prime necessity for
physicians and health workers to recognize accurate growth rate in number of
diabetic patients. Artificial Neural Network is used as one of the artificial intelligent
technique for forestalling growth rate of type II diabetic patients. Diabetes occurred
due to increased level of glucose in blood. In this paper, an intense survey is done for
the prediction of Type II diabetes using different Data Mining tools and Artificial
Neural Network techniques, is presented. This survey is aimed to recognize and
propose an effective technique for earlier prediction of the Type II diabetes. The data
mining techniques like C4.5 Classifier, Support Vector Machine and K-Nearest
Neighbour are compared for this work with Artificial Neural Network. As the results
Artificial Neural Network found with a great accuracy of 89%.
IRJET- Smart Medical Assistant using Wearable DeviceIRJET Journal
This document proposes a smart medical assistant system using a wearable device to monitor health and predict medical emergencies like heart attacks. The system has 5 modules: 1) a hardware module containing sensors to monitor heart rate and temperature, 2) a web application for hospitals, 3) a heart attack prediction module using machine learning, 4) a mobile application for patients and ambulances, and 5) an API module connecting the web and mobile applications. The system aims to detect medical emergencies using sensor data, alert hospitals and contacts, and provide health advice and ambulance tracking to assist users.
IRJET - Comparative Study of Cardiovascular Disease Detection AlgorithmsIRJET Journal
The document compares four algorithms - K-Nearest Neighbors, Support Vector Machine, Decision Tree, and Random Forest - for cardiovascular disease detection using data mining techniques. It summarizes previous studies that have used these algorithms on cardiovascular disease data and evaluated their performance. The document concludes that K-Nearest Neighbors, Support Vector Machine, Decision Tree, or Random Forest algorithms could be used for cardiovascular disease detection, and that the best algorithm depends on the specific dataset and type of disease being diagnosed.
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.
Artificial intelligence (AI) is a fast-growing field and its applications to diabetes, a global pandemic, can reform the approach to diagnosis and management of this chronic condition. Principles of machine learning have been used to build algorithms to support predictive models for the risk of developing diabetes or its consequent complications. Digital therapeutics have proven to be an established intervention for lifestyle therapy in the management of diabetes. Patients are increasingly being empowered for self-management of diabetes, and both patients and health care professionals are benefitting from clinical decision support. AI allows a continuous and burden-free remote monitoring of the patient's symptoms and biomarkers. Further, social media and online communities enhance patient engagement in diabetes care. Technical advances have helped to optimize resource use in diabetes. Together, these intelligent technical reforms have produced better glycemic control with reductions in fasting and postprandial glucose levels, glucose excursions, and glycosylated hemoglobin. AI will introduce a paradigm shift in diabetes care from conventional management strategies to building targeted data-driven precision care.
IRJET - Breast Cancer Prediction using Supervised Machine Learning Algorithms...IRJET Journal
This document describes a study that uses supervised machine learning algorithms to predict breast cancer. Three algorithms - decision tree, logistic regression, and random forest - are applied to preprocessed breast cancer data. The random forest model achieved the best accuracy at 98.6% for predicting whether a tumor was benign or malignant. The study aims to develop an early prediction system for breast cancer using machine learning techniques.
MLTDD : USE OF MACHINE LEARNING TECHNIQUES FOR DIAGNOSIS OF THYROID GLAND DIS...cscpconf
Machine learning algorithms are used to diagnosis for many diseases after very important improvements of classification algorithms as well as having large data sets and high performing computational units. All of these increased the accuracy of these methods. The diagnosis of thyroid gland disorders is one of the application for important classification problem. This study majorly focuses on thyroid gland medical diseases caused by underactive or overactive thyroid glands. The dataset used for the study was taken from UCI repository. Classification of this thyroid disease dataset was a considerable task using decision tree algorithm. The overall
prediction accuracy is 100% for training and in range between 98.7% and 99.8% for testing. In this study, we developed the Machine Learning tool for Thyroid Disease Diagnosis (MLTDD), an Intelligent thyroid gland disease prediction tool in Python, which can effectively help to make the right decision, has been designed using PyDev, which is python IDE for Eclipse.
Heart Disease Identification Method Using Machine Learnin in E-healthcare.SUJIT SHIBAPRASAD MAITY
This document describes a student project that aims to develop a machine learning model for heart disease identification and prediction. It discusses existing heart disease diagnosis techniques, identifies the problem and requirements, outlines the proposed algorithm and methodology using supervised learning classification algorithms like K-Nearest Neighbors and logistic regression. Block diagrams and flow charts illustrate the data preprocessing, model training, and web application development steps to classify patients as having heart disease or not and evaluate model performance. The developed system achieves high accuracy for heart disease prediction.
This document describes a heart disease prediction system that uses machine learning algorithms to analyze patient data and predict the presence and severity of heart disease. The system uses four algorithms - random forest, naive bayes, decision tree, and linear regression - to build predictive models using a dataset of 801 patients with 12 medical attributes. The models are evaluated on their accuracy in both detecting heart disease and classifying its severity from 0 to 4. Random forest achieved the highest accuracy of 95.09% while naive bayes had the lowest at 60.38%. The system provides a way to more accurately diagnose heart disease early using data mining of existing patient records.
ody Information Analysis based Personal Exercise Management System IJECEIAES
Recently, people's interest in health is deepening. So health-related systems are being developed. Existing exercise management systems provided users with exercise related information using PC or smart phone. However, there is a problem that the accuracy of the algorithm for analyzing the user's body information and providing information is low.In this paper, we analyze users' body mass index (BMI) and basal metabolic rate (BMR) and we propose a system that provides the user with necessary information through recommendation algorithm. It informs the user of exercise intensity and momentum, and graphs the exercise history of the user. It also allows the user to refer to the fitness history of other users in the same BMI group. This allows the user to receive more personalized services than the existing exercise management system, thereby enabling efficient exercise.
A major challenge facing healthcare organizations (hospitals, medical centers) is
the provision of quality services at affordable costs. Quality service implies diagnosing
patients correctly and administering treatments that are effective. Poor clinical decisions
can lead to disastrous consequences which are therefore unacceptable. Hospitals must
also minimize the cost of clinical tests. They can achieve these results by employing
appropriate computer-based information and/or decision support systems.
Most hospitals today employ some sort of hospital information systems to manage
their healthcare or patient data.
These systems are designed to support patient billing, inventory management and generation of simple statistics. Some hospitals use decision support systems, but they are largely limited. Clinical decisions are often made based on doctors’ intuition and experience rather than on the knowledge rich data hidden in the database.
This practice leads to unwanted biases, errors and excessive medical costs which affects the quality of service provided to patients.
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.
Disease Prediction And Doctor Appointment systemKOYELMAJUMDAR1
This document outlines a disease prediction and doctor appointment system using machine learning. The objectives are to provide quick medical diagnosis to rural patients and enhance access to medical specialists. Five machine learning algorithms - Decision Tree, Random Forest, Naive Bayes, K-Nearest Neighbors, and Support Vector Machine - are used for disease prediction. The system displays predicted diseases and accuracy scores for each algorithm. Users can then book appointments with specialist doctors for their predicted disease.
One of the major purposes manufacturers incorporate AI or ML in their applications is to ease software computations and to predict precise results. I think compared to any other application, a medical application requires a lot of precise computations and therefore, AI is a perfect solution to enhance performance and productivity. While reading the health-tech news, I came across recent research in this regard, the use of AI in predicting a potential stroke or cardiac arrest. ..
This document provides a software requirements specification for an Attendance Management System being developed for JSS Academy of Technical Education. It includes sections on introduction and purpose, general description of product functions and users, specific requirements including functional and non-functional requirements, and analysis models including sequence diagrams, data flow diagrams, and state transition diagrams. The system will allow for student registration and management of attendance, and provide reports. It is intended to help streamline administrative tasks for the educational institution.
Supervised machine learning based liver disease prediction approach with LASS...journalBEEI
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1. Performance Analysis of Support Vector Machine in
Diabetes Prediction
Vinod Jain
Assistant Professor, Department of Computer Engineering
Applications
GLA University , Mathura, India
Vinod.jain@gla.ac.in
(ORCHID-0000-0003-0260-7319)
Narendra Mohan
Assistant Professor, Department of Computer Engineering
Applications
GLA University , Mathura, India
narendra.mohan@gla.ac.in
(ORCHID- 0000-0002-7037-3318)
Abstract—Lots of people are suffering from diabetes in India.
The disease is very serious and cause many other problems in
human body. Many factors are cause of this disease in human
body. The disease is not curable and can only be controlled. In
this paper Support Vector Machine learning algorithm is applied
in prediction of diabetes. The performance of SVM algorithm is
analyzed for different available kernels. The best kernel is
selected and used for prediction. The proposed work is
implemented in python programming language and its
performance is found good as compared to other algorithms.
Keywords—Machine Learning; Support Vector Machine;
Diabetes Prediction
I. INTRODUCTION
Diabetes is a very common disease in India now a day. The
life of diabetic patient is not easy at all. According to WHO
there were almost 31.7 million diabetic patients in India in the
year 2000 and it may goes to 79.4 million by 2030. Figure 1 is
showing the WHO data of diabetic patients in India. There is a
need to control this disease in India.
Fig. 1. WHO report on diabetes
Machine learning algorithm are mathematical techniques
which are very useful in analyzing large amount of data and
suggesting some actions on the basis of that data. These
algorithms are also very useful in analyzing a data set and
predicting values for a new entry. Many researchers [1][5][6]
are applying machine learning algorithms for prediction and
control of various diseases. The results of machine learning
algorithms found very good in prediction of different diseases.
II. LITERATURE SURVEY
J. Neelaveni and M. S. G. Devasana [1] applied machine
learning for Alzheimer prediction. V. K. Yarasuri et al. [2]
proposed a machine leaning based model for Hepatitis
prediction. M. P. N. M. Wickramasinghe et al. [3] applied
machine learning algorithms for diet prediction. The system
was used for dietary prediction for kidney diseases. A. Maurya
et al. [4] also applied ML for recommending diet plan for
patients suffering from kidney disease prediction. V. Vats et al.
[5] proposed an approach for prediction of liver diseases using
machine learning.
A. Gavhane et al. [6] proposed a system for prediction of
heart diseases. The machine learning algorithms was used for
prediction of heart diseases. The accuracy of machine learning
algorithms was tested on a data set of heart diseases. S. K. J.
and G. S. [7] also applied machine learning based approach for
heart disease prediction.
Support Vector Machine (SVM) is a very popular machine
learning model. It works on supervised machine learning
model. In supervised machine learning model we have a
teacher and the model is trained on the instructions of a
teacher/critic. It is very useful in solving classification
problems.
A lot of other authors [8-17] also applied machine learning
algorithm in prediction and detection of various diseases.
Support Vector Machine is also applied by many researchers to
predict various diseases [14-17]. But there is a scope of
research to optimize the performance of SVM algorithm in
prediction of diabetes patients in Indian context. The next
section discusses the proposed work for diabetes detection
using SVM.
2. III. PROPOSED METHODOLOGY
This paper applied SVM machine learning algorithm in
diabetes prediction. The SVM algorithm is implemented in
python programming language and tested on a data set. The
SVM model is created using python programming language.
The dataset is divided into training set and testing set. Then the
SVM model is trained.
Fig. 2. Proposed Methodology
The model is trained on four different kernels available for
SVM and its prediction accuracies are calculated by testing set.
The SVM is tested on four kernels which are Linear kernel,
Polynomial kernel, Sigmoid Kernel and RBF kernel. The best
SVM kernel is selected and used for diabetes prediction. Figure
2 is showing the flowchart of the proposed model.
The proposed model is implemented in Python
programming language and tested on a data set of 768 patients.
The data set is freely available on Kaggle with the name Pima
Indians Diabetes Database for research. The data set is
available in the form of a CSV file which is best suited for
python programming language.
The accuracy of the SVM model depends on the selection
of a particular model. The available models are Linear model,
Polynomial model, RBF model and Sigmoid model. First the
SVM is trained and tested on different models.
IV. RESULTS ANALYSIS
The Table 1 is showing the accuracy of the SVM for
different available models such as Linear, Polynomial, RBF
and Sigmoidal. The accuracy is found maximum for RBF
model which is 82%.
TABLE I. Accuracy of different SVM kernels
SVM Kernel Accuracy
Linear 0.77
Polynomial 0.80
RBF 0.82
Sigmoid 0.69
Fig. 3. Comparison of prediction accuracy of SVM kernels
Figure 3 is showing the bar chart of the prediction accuracy
of SVM Kernels. It is observed that the prediction accuracy of
RBF kernel is maximum for SVM while predicting diabetes for
Indian patients.
3. V. CONCLUSION AND FUTURE SCOPE
This paper proposed a machine learning based model for
diabetes prediction. Support Vector Machine model is used in
diabetes prediction. The four kernels of the SVM are used for
prediction and their prediction accuracy is measured. It is
found that the RBF kernel best performs for the diabetes
prediction of Indian patients as its prediction accuracy is found
best among the four kernels. In future the RBF SVM kernel can
be tested in prediction of other diseases such as Cancer,
Thyroid etc.
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