Biometrics and Bioinformatics

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Comparative Analysis of Various Classification and Clustering Algorithms for Heart Disease Prediction System


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1 Department of Computer Science, Rathinam College of Arts & Science (Autonomous), Coimbatore-641021, Tamil Nadu, India
     

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Medical science industry has huge amount of data, but unfortunately most of this data is not mined to find out hidden information in data. Advanced data mining techniques can be used to discover hidden pattern in data. These techniques will be useful for medical practitioners to take effective decision. In this research paper, data mining classification techniques RIPPER classifier, Decision tree, Artificial Neural Network (ANN), Naive Bayes, Support Vector Machine (SVM), are analyzed on heart disease dataset. Performance of these techniques is compared through sensitivity, specificity, Accuracy, true positive Rate and False positive Rate. This analysis shows that out of these five classification techniques methods SVM predicts highest accuracy, Specificity, Sensitivity. And it’s also gives the better result on True positive Rate is high False and positive Rate is low.


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  • Comparative Analysis of Various Classification and Clustering Algorithms for Heart Disease Prediction System

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Authors

S. Shylaja
Department of Computer Science, Rathinam College of Arts & Science (Autonomous), Coimbatore-641021, Tamil Nadu, India
R. Muralidharan
Department of Computer Science, Rathinam College of Arts & Science (Autonomous), Coimbatore-641021, Tamil Nadu, India

Abstract


Medical science industry has huge amount of data, but unfortunately most of this data is not mined to find out hidden information in data. Advanced data mining techniques can be used to discover hidden pattern in data. These techniques will be useful for medical practitioners to take effective decision. In this research paper, data mining classification techniques RIPPER classifier, Decision tree, Artificial Neural Network (ANN), Naive Bayes, Support Vector Machine (SVM), are analyzed on heart disease dataset. Performance of these techniques is compared through sensitivity, specificity, Accuracy, true positive Rate and False positive Rate. This analysis shows that out of these five classification techniques methods SVM predicts highest accuracy, Specificity, Sensitivity. And it’s also gives the better result on True positive Rate is high False and positive Rate is low.


References