Prediction of Coronary Artery Disease Using a Combination of Methods for Training Radial Basis Function Networks

Abstract

Cardiovascular disease (CAD) is among the most prevalent diseases around the world; nevertheless, its diagnosis requires highly qualified medical staff (e.g., cardiologists) because of the many variables involved in the process. Due to diagnostic complexity and the limited number of available qualified staff, the development of smart systems that could automate the diagnostic process is paramount. This paper investigates two systems in order to achieve this goal.