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An Ensemble Classifier for the Prediction of Heart Disease

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  • Ria A Kurian

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.

Suggested Citation

  • Ria A Kurian, 2018. "An Ensemble Classifier for the Prediction of Heart Disease," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 3(6), pages 25-31, June.
    • Handle: RePEc:jbh:ijsrcs:v3:y2018:i6:id:hcseit1835269
    Note: Article URL: https://ijsrcseit.com/CSEIT1835269
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