Predicting Coronary Artery Diseases using Effective Features Selected by Harris Hawks Optimization Algorithm and Support Vector Machine

With ۱۷ million annual deaths, cardiovascular diseases are the leading cause of mortality across the world with coronary artery disease (CAD) as the most prevalent one. CAD is the leading cause of death in industrial countries and at the same time is rapidly spreading in the developing world. Thus, the development and introduction of machine learning methods for the accurate diagnosis of heart diseases, especially CAD, have been an important debate in recent years in order to overcome relevant problems. The aim of this paper was to propose a model for enhancing CAD prediction accuracy. It sought a framework for predicting and diagnosing CAD using the features selection of Harris Hawks Optimization algorithm (HHO) and Support Vector Machine (SVM). The heart disease data set of Cleveland hospital available in the University of California Irvine (UCI) was used as the studied data set. It included ۳۰۳ cases. Each case had ۱۴ features with the final medical status of cases (CAD or normal case) as one of the features where ۱۶۵ and ۱۳۸ cases were diagnosed as CAD and normal, respectively. The results of this study revealed that HHO could enhance CAD diagnosis accuracy.

Keywords:

Coronary Artery Diseases , feature selection , Harris Hawk Optimization algorithm , support vector machine.