Prediction of Coronary Artery Disease Using Enhanced Feature Selection Using Firefly Based Optimization

Abstract

Coronary Artery Disease (CAD) is an uncontrolled deadly disease that causes unexpected sudden death and it is rapidly increasing among people. CAD is becoming most common in middle age and old age people. So far angiography is considered as a better method to diagnose CAD, but it leads to severe side effects and highly expensive. Multiple researches have been conducted with the techniques involving data mining and machine learning to predict and diagnose CAD. In this paper, Enhanced Feature Selection based on Firefly Optimization (EFS-PO) is proposed for increasing the prediction of CAD. In EFS-PO, the performance of feature selection is enhanced by firefly optimization that ends with enhanced classification accuracy. Weights of the feature are enhanced via firefly optimization towards better selection. EFS-PO has achieved better results in terms of all considered benchmark data mining performance metrics.