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Reseach Article

Heart Attack Analysis and Prediction using SVM

by Madhu H.K., D. Ramesh
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 183 - Number 27
Year of Publication: 2021
Authors: Madhu H.K., D. Ramesh

Madhu H.K., D. Ramesh . Heart Attack Analysis and Prediction using SVM. International Journal of Computer Applications. 183, 27 ( Sep 2021), 35-39. DOI=10.5120/ijca2021921658

@article{ 10.5120/ijca2021921658,
author = { Madhu H.K., D. Ramesh },
title = { Heart Attack Analysis and Prediction using SVM },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2021 },
volume = { 183 },
number = { 27 },
month = { Sep },
year = { 2021 },
issn = { 0975-8887 },
pages = { 35-39 },
numpages = {9},
url = { },
doi = { 10.5120/ijca2021921658 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-07T01:18:04.451355+05:30
%A Madhu H.K.
%A D. Ramesh
%T Heart Attack Analysis and Prediction using SVM
%J International Journal of Computer Applications
%@ 0975-8887
%V 183
%N 27
%P 35-39
%D 2021
%I Foundation of Computer Science (FCS), NY, USA

Smart gadgets from tiny oximeter to wrist watches collect data from human body to analyse and predict future occurrences. The most wanted model for this high active environment is the prediction model. Many algorithms have been developed by various researchers and today tools are available in software like MATLAB, Phyton and Tenser flow. In this paper SVM a supervised model is implemented to predict heart attack. The 13 features are considered which include personal details like chest pain type, blood pressure, collestral level and heart rate. The implemented model is tested on UCI health care heart disease data set. The efficacy of the model proposed is justified using performance and confusion matrix. The accuracy obtained is 83%.

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Index Terms

Computer Science
Information Sciences


Machine Learning (ML) Support Vector Machines (SVM) UCI Health Care Dataset.