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Accuracy Enhancement of Artificial Neural Network using Genetic Algorithm

by Preeti Gupta, Bikrampal Kaur
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 103 - Number 13
Year of Publication: 2014
Authors: Preeti Gupta, Bikrampal Kaur
10.5120/18133-9258

Preeti Gupta, Bikrampal Kaur . Accuracy Enhancement of Artificial Neural Network using Genetic Algorithm. International Journal of Computer Applications. 103, 13 ( October 2014), 11-15. DOI=10.5120/18133-9258

@article{ 10.5120/18133-9258,
author = { Preeti Gupta, Bikrampal Kaur },
title = { Accuracy Enhancement of Artificial Neural Network using Genetic Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { October 2014 },
volume = { 103 },
number = { 13 },
month = { October },
year = { 2014 },
issn = { 0975-8887 },
pages = { 11-15 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume103/number13/18133-9258/ },
doi = { 10.5120/18133-9258 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:34:27.111140+05:30
%A Preeti Gupta
%A Bikrampal Kaur
%T Accuracy Enhancement of Artificial Neural Network using Genetic Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 103
%N 13
%P 11-15
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This research paper proposes the enhancement of the accuracy of the results by using Artificial Neural Network optimized with Genetic Algorithm in prediction of heart disease diagnosis with UCI dataset. In this study neural network is optimized with Genetic Algorithm and proved experimentally. The trained feed forward neural network and fitting neural network are optimized with genetic algorithm and is then compared with the scale conjugate gradient descent back-propagation algorithms trained feed forward neural network and fitting neural network respectively for the accuracy enhancement percentage. The proposed learning is much faster and accurate as compared to the other one. The proposed learning is designed and developed by using MATLAB GUI feature. The proposed method achieved an accuracy of 97. 83%. With this higher achieved accuracy the heart disease can be diagnosed more accurately and much proper treatments can be suggested.

References
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  9. A. Frank and A. Asuncion, "UCI Machine Learning Repository", University of California, Irvine, School of Information and Computer Sciences, 2010.
  10. R. Detrano, A. Janosi, W. Steinbrunn, M. Pfisterer, J. -J. Schmid, S. Sandhu, K. H. Guppy, S. Lee, and V. Froelicher, "International application of a new probability algorithm for the diagnosis of coronary artery disease", The American journal of cardiology, vol. 64, no. 5, pp. 304–310, 1989.
Index Terms

Computer Science
Information Sciences

Keywords

Data Mining Diagnosis Genetic Algorithm Heart Disease Neural Network.

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