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Reseach Article

An Experimental Study of Various Machine Learning Approaches in Heart Disease Prediction

by Md. Shafiul Azam, Md. Abu Raihan, Humayan Kabir Rana
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 175 - Number 21
Year of Publication: 2020
Authors: Md. Shafiul Azam, Md. Abu Raihan, Humayan Kabir Rana
10.5120/ijca2020920741

Md. Shafiul Azam, Md. Abu Raihan, Humayan Kabir Rana . An Experimental Study of Various Machine Learning Approaches in Heart Disease Prediction. International Journal of Computer Applications. 175, 21 ( Sep 2020), 16-21. DOI=10.5120/ijca2020920741

@article{ 10.5120/ijca2020920741,
author = { Md. Shafiul Azam, Md. Abu Raihan, Humayan Kabir Rana },
title = { An Experimental Study of Various Machine Learning Approaches in Heart Disease Prediction },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2020 },
volume = { 175 },
number = { 21 },
month = { Sep },
year = { 2020 },
issn = { 0975-8887 },
pages = { 16-21 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume175/number21/31576-2020920741/ },
doi = { 10.5120/ijca2020920741 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:25:39.992953+05:30
%A Md. Shafiul Azam
%A Md. Abu Raihan
%A Humayan Kabir Rana
%T An Experimental Study of Various Machine Learning Approaches in Heart Disease Prediction
%J International Journal of Computer Applications
%@ 0975-8887
%V 175
%N 21
%P 16-21
%D 2020
%I Foundation of Computer Science (FCS), NY, USA
Abstract

According to recent survey of WHO (World Health Organization) 17.9 million people die each year because of heart related diseases and it is increasing rapidly. With the increasing population and diseases, it has become challenging to diagnosis and treatment diseases at the right time. But there is a light of hope that recent advancements in technology have accelerated the public health sector by advanced functional biomedical solutions. This paper aims to analyze the various machine learning approaches namely Naïve Bayes (NB), Random Forest (RF) Classification, Decision tree (DT), Support Vector Machine (SVM) and Logistic Regression (LR) by employing a qualified dataset for heart disease prediction. This research finds the correlations between the various attributes that are suitable to predict the chances of a heart disease and compares the impact of Principle Component Analysis (PCA) on the accuracy of the above mentioned algorithms.

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Index Terms

Computer Science
Information Sciences

Keywords

Heart Disease Machine Learning Algorithms PCA Decision Tree SVM Random Forest Logistic Regression Naïve Bayes