Analysis of CAS Using Improved PSO with Fuzzy Based System

Coronary artery Syndrome (CAS) is myocardial infarction angina pectoris, CAS is one of the most common public syndrome in our country. Coronary artery syndrome involves the occurrence of pathological changes, arteriosclerosis, in the partitions of one or more of the coronary vessels. Physical immobility is a potent risk factor for coronary artery disease, but old age, high blood pressure, blood lipid disorders, male gender and heredity, as well as smoking, diabetes and overweight also increase the risk of developing the syndrome. The main aim of this paper demonstrates a fuzzy based coronary artery syndrome (CAS) analysis system using advanced improved Particle Swarm Optimization. In this proposed system based on the Cleveland and Hungarian Heart syndrome datasets. Since the datasets having numerous input points, decision tree (DT) was employed to loosen the characteristics that give towards the study. The output of the decision tree was adjusted into crisp if– then rules and then altered into fuzzy rule base. Improved Particle Swarm Optimization (IPSO) was utilized to correct the fuzzy membership functions (MFs).