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Computer aided decision making for heart disease detection using hybrid neural network-Genetic algorithm

Version 2 2024-06-05, 05:52
Version 1 2019-07-03, 13:33
journal contribution
posted on 2024-06-05, 05:52 authored by Z Arabasadi, Roohallah AlizadehsaniRoohallah Alizadehsani, M Roshanzamir, H Moosaei, AA Yarifard
Cardiovascular disease is one of the most rampant causes of death around the world and was deemed as a major illness in Middle and Old ages. Coronary artery disease, in particular, is a widespread cardiovascular malady entailing high mortality rates. Angiography is, more often than not, regarded as the best method for the diagnosis of coronary artery disease; on the other hand, it is associated with high costs and major side effects. Much research has, therefore, been conducted using machine learning and data mining so as to seek alternative modalities. Accordingly, we herein propose a highly accurate hybrid method for the diagnosis of coronary artery disease. As a matter of fact, the proposed method is able to increase the performance of neural network by approximately 10% through enhancing its initial weights using genetic algorithm which suggests better weights for neural network. Making use of such methodology, we achieved accuracy, sensitivity and specificity rates of 93.85%, 97% and 92% respectively, on Z-Alizadeh Sani dataset.

History

Journal

Computer methods and programs in biomedicine

Volume

141

Pagination

19-26

Location

Amsterdam, The Netherlands

ISSN

0169-2607

eISSN

1872-7565

Language

eng

Publication classification

C1.1 Refereed article in a scholarly journal

Copyright notice

2017, Elsevier B.V.

Publisher

Elsevier