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Using Artificial Neural Networks to Diagnose Heart Disease

by Ahmad Rufai, U. S. Idriss, Mahmood Umar
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 182 - Number 19
Year of Publication: 2018
Authors: Ahmad Rufai, U. S. Idriss, Mahmood Umar
10.5120/ijca2018917938

Ahmad Rufai, U. S. Idriss, Mahmood Umar . Using Artificial Neural Networks to Diagnose Heart Disease. International Journal of Computer Applications. 182, 19 ( Oct 2018), 1-6. DOI=10.5120/ijca2018917938

@article{ 10.5120/ijca2018917938,
author = { Ahmad Rufai, U. S. Idriss, Mahmood Umar },
title = { Using Artificial Neural Networks to Diagnose Heart Disease },
journal = { International Journal of Computer Applications },
issue_date = { Oct 2018 },
volume = { 182 },
number = { 19 },
month = { Oct },
year = { 2018 },
issn = { 0975-8887 },
pages = { 1-6 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume182/number19/30038-2018917938/ },
doi = { 10.5120/ijca2018917938 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:11:49.491746+05:30
%A Ahmad Rufai
%A U. S. Idriss
%A Mahmood Umar
%T Using Artificial Neural Networks to Diagnose Heart Disease
%J International Journal of Computer Applications
%@ 0975-8887
%V 182
%N 19
%P 1-6
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper presents an application of Artificial Neural Networks (ANN) in predicting patient coronary heart disease status. Multilayer perceptron (MLP) which is a type of ANN architecture was used to develop the proposed model. Several experiments were carried out to determine the network optimal parameters. Overall, the optimised ANN system achieved a very high diagnosing accuracy of 92.2%, proving its usefulness in support of diagnosis process of coronary heart disease.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Artificial neural networks Multilayer perceptron Back-propagation algorithm Coronary heart disease Principal Component Analysis

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