Computer aided decision making for heart disease detection using hybrid neural network-Genetic algorithm. (April 2017)

• Record Type: Journal Article
• Authors: Arabasadi, Zeinab; Alizadehsani, Roohallah; Roshanzamir, Mohamad; Moosaei, Hossein; Yarifard, Ali Asghar
• Is Part Of: Computer methods and programs in biomedicine. Volume 141(2017)
• Page Start: 19
• Page End: 26
• Publication Date: 2017-04
• Subjects: Cardiovascular disease -- Coronary artery disease -- Neural network -- Genetic algorithm; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2017.01.004
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: A hybrid method was proposed in this research that combines genetic algorithm and neural network. The proposed method could increase the performance of neural network. The proposed method could reach very good accuracy rate in coronary artery disease diagnosis. 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.