Parsimonious Evolutionary-based Model Development for Detecting Artery Disease

Abstract

Coronary artery disease (CAD) is the most common cardiovascular condition. It often leads to a heart attack causing millions of deaths worldwide. Its accurate prediction using data mining techniques could reduce treatment risks and costs and save million lives. Motivated by these, this study proposes a framework for developing parsimonious models for CAD detection. A novel feature selection method called weight by Support Vector Machine is first applied to identify most informative features for model development. Then two evolutionary-based models called genetic programming expression (GEP) and genetic algorithm-emotional neural network (GA-ENN) are implemented for CAD prediction. Obtained results indicate that the GEP models outperform GA-ENN models and achieve the state of the art accuracy of 90%. Such a precise model could be used as an assistive tool for medical diagnosis as well as training purposes.