An Ensemble Classifier for the Prediction of Heart Disease

Authors(2) :-Ria A Kurian, Lakshmi K.S

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 patientsí 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.

Authors and Affiliations

Ria A Kurian
Department of Information and Technology, Rajagiri School of Engineering and Technology, Ernakulam, Kerala, India
Lakshmi K.S
Department of Information and Technology, Rajagiri School of Engineering and Technology, Ernakulam, Kerala, India

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

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Publication Details

Published in : Volume 3 | Issue 6 | July-August 2018
Date of Publication : 2018-06-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 25-31
Manuscript Number : CSEIT1835269
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Ria A Kurian, Lakshmi K.S, "An Ensemble Classifier for the Prediction of Heart Disease", International 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.
Journal URL : http://ijsrcseit.com/CSEIT1835269

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