An Ensemble Classifier for the Prediction of Heart Disease

Authors

  • Ria A Kurian   Department of Information and Technology, Rajagiri School of Engineering and Technology, Ernakulam, Kerala, India

Keywords:

Heart Disease, KNN, decision Tree, Naive Bayes, Classifier Ensemble, Majority Voting.

Abstract

Heart disease has become a silent killer among people of all ages. The major risk factors of heart disease include smoking, blood pressure, cholesterol, diabetes etc. Early diagnosis and treatment can reduce morbidity rate to an extent by identifying patients at higher risk of having a heart disease and providing them right care at right time. However provisioning of quality services at reasonable costs is a major concern of every healthcares. Poor clinical decisions can pose adverse effects on human health. This paper introduces a method based on data mining according to the information of patients’ medical records to predict heart disease. An ensemble classifier approach is being used, that is the combination of three classifiers ( KNN, Decision Tree, NaiveBayes ) composing an ensemble, so that the overall model can be used to give predictions with greater accuracy than that of individual classifiers.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2018-06-30

Issue

Volume 3, Issue 6, July-August-2018

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Ria A Kurian , " An Ensemble Classifier for the Prediction of Heart Disease, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 6, pp.25-31, July-August-2018.