An ensemble PSO-based approach for diagnosis of coronary artery disease

The leading causes of heart failure are diseases that damage the heart. One of the most well-known diseases that cause heart failure is Coronary Artery Disease. Diagnosis of Coronary Artery Disease is an important medical problem. Many researchers have tried to develop intelligent medical systems to increase the ability of physicians in detecting this disease. Particle Swarm Optimization (PSO) has been successfully applied in data mining field to extract rule based classification systems. A new ensemble PSO-based approach to extract a set of rules for diagnosis of coronary artery disease is presented in this paper. The boosting method considers the cooperation between fuzzy rules that generate with PSO meta-heuristic. We called this approach as "EP-DC". We have evaluated our new classification approach via the well-known Cleveland data set. Results show that the proposed learning method can detect the coronary artery disease with an acceptable accuracy. In addition, the discovered rules have also considerable comprehensibility.