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

^*Mashail Alsalamah
Development, Faculty Of Engineering And Computing, Coventry University, United Kingdom

^*Corresponding Author:

Mashail Alsalamah
Development, Faculty Of Engineering And Computing, Coventry University, United Kingdom
Email:alsalam2@coventry.ac.uk

Published on: 2016-11-14

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.

Keywords

Extended kalman filter; Coronary artery disease; Radial basis function networks; Particle swarm optimization; Gravitational search algorithm

Introduction

Modern lifestyle habits have significantly increased incidents of cardiovascular disease. Qualified staff available for disease diagnosis in this medical area remains limited and, therefore, are under increased pressure. Fortunately, the diagnosis of complex diseases has become much easier due to progress in computing technologies and artificial intelligence using patient information and the manifestation of their symptoms.