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Cited by 499

The DOI matches its Crossref record; 59 of the 59 references it cites were found in a registry or an index.

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

Open access
Arabasadi, Zeinab, Alizadehsani, Roohallah, Roshanzamir, Mohamad, Moosaei, Hossein, Yarifard, Ali Asghar·2017·Computer Methods and Programs in Biomedicine
DOI 10.1016/j.cmpb.2017.01.004
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Abstract

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.

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Metadata

Authors
Arabasadi, Z., Alizadehsani, R., Roshanzamir, M., Moosaei, H., & Yarifard, A. A.
Venue
Computer Methods and Programs in Biomedicine
Publisher
Elsevier BV
Published
2017-04
Volume
141
Pages
19-26
ISSN
0169-2607 (ISSN-L)
DOI
10.1016/j.cmpb.2017.01.004
OpenAlex
W2579725890
Cited by
499
References
59
FWCI
49.80
Type
journal article
Language
en
Open access
green

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