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Reseach Article

A Data mining Model for predicting the Coronary Heart Disease using Random Forest Classifier

Published on April 2012 by A. Sheik Abdullah, R. R. Rajalaxmi
International Conference in Recent trends in Computational Methods, Communication and Controls
Foundation of Computer Science USA
ICON3C - Number 3
April 2012
Authors: A. Sheik Abdullah, R. R. Rajalaxmi
2c0649ec-33ba-4081-aacf-e6f52c194ac4

A. Sheik Abdullah, R. R. Rajalaxmi . A Data mining Model for predicting the Coronary Heart Disease using Random Forest Classifier. International Conference in Recent trends in Computational Methods, Communication and Controls. ICON3C, 3 (April 2012), 22-25.

@article{
author = { A. Sheik Abdullah, R. R. Rajalaxmi },
title = { A Data mining Model for predicting the Coronary Heart Disease using Random Forest Classifier },
journal = { International Conference in Recent trends in Computational Methods, Communication and Controls },
issue_date = { April 2012 },
volume = { ICON3C },
number = { 3 },
month = { April },
year = { 2012 },
issn = 0975-8887,
pages = { 22-25 },
numpages = 4,
url = { /proceedings/icon3c/number3/6020-1021/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference in Recent trends in Computational Methods, Communication and Controls
%A A. Sheik Abdullah
%A R. R. Rajalaxmi
%T A Data mining Model for predicting the Coronary Heart Disease using Random Forest Classifier
%J International Conference in Recent trends in Computational Methods, Communication and Controls
%@ 0975-8887
%V ICON3C
%N 3
%P 22-25
%D 2012
%I International Journal of Computer Applications
Abstract

Coronary Heart Disease (CHD) is a common form of disease affecting the heart and an important cause for premature death. From the point of view of medical sciences, data mining is involved in discovering various sorts of metabolic syndromes. Classification techniques in data mining play a significant role in prediction and data exploration. Classification technique such as Decision Trees has been used in predicting the accuracy and events related to CHD. In this paper, a Data mining model has been developed using Random Forest classifier to improve the prediction accuracy and to investigate various events related to CHD. This model can help the medical practitioners for predicting CHD with its various events and how it might be related with different segments of the population. The events investigated are Angina, Acute Myocardial Infarction (AMI), Percutaneous Coronary Intervention (PCI), and Coronary Artery Bypass Graft surgery (CABG). Experimental results have shown that classification using Random Forest Classification algorithm can be successfully used in predicting the events and risk factors related to CHD.

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Index Terms

Computer Science
Information Sciences

Keywords

Coronary Heart Disease Decision Trees Random Forest