An expert system working upon an ensemble PSO-based approach for diagnosis of coronary artery disease

It is evident that usage of data mining methods in disease diagnosis has been increasing gradually. In this paper, diagnosis of Coronary Artery Disease, which is one of the most well-known diseases that cause heart failure, was conducted with such a data mining system. Many researchers have attempted to develop a medical expert system to increase the ability of physicians in detecting this disease. This paper proposes a new ensemble PSO-based approach to extract a set of rules for diagnosis of coronary artery disease. The new presented boosting mechanism considers the cooperation between generated fuzzy if-then rules using the PSO meta-heuristic. We have evaluated our new classification approach using the well-known Cleveland data set. Results indicate that the proposed learning method can detect the coronary artery disease with an acceptable accuracy. In addition, the extracted fuzzy rules have significant interpretability either.