Decision Making for Heart Disease Detection Using Hybrid Neural Network-Particle Swarm Optimization Algorithm

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 (genetic algorithm) and data mining 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 through enhancing its initial weights using Particle Swarm optimization Algorithm (PSO) that suggests better weights for neural network. Making use of such methodology, we can improve accuracy, sensitivity and specificity rates on Z-Alizadeh Sani dataset.