A method for estimating the risk of a heart attack using imperialist competitive algorithm and neural networks

The mortality from coronary heart disease is much higher than those from natural events. The World Health Organization (WHO) estimates that around 17 million deaths are due to heart and artery attacks. It should be noted that coronary heart disease is one of the main causes of mortality in advanced and developed countries such as Iran. Several methods have been proposed for the estimation and recognition of the risk of heart attacks, each of which has several advantages and disadvantages. Some disadvantages are as follows: low accuracy in the diagnosis of risk factors for coronary artery disease, time costs for selecting appropriate features, large number of diagnostic parameters, and the possibility of error. In this paper, we evaluate some of these methods and their advantages and disadvantages. The main scope is to review the methods and their advantages and disadvantages.