Using PSO Algorithm for Producing Best Rules in Diagnosis of Heart Disease

Heart disease is still a growing global health issue. In the health care system, limiting human experience and expertise in manual diagnosis leads to inaccurate diagnosis, and the information about various illnesses is either inadequate or lacking in accuracy as they are collected from various types of medical equipment. Since the correct prediction of a person's condition is of great importance, equipping medical science with intelligent tools for diagnosing and treating illness can reduce doctors' mistakes and financial losses. In this paper, the Particle Swarm Optimization (PSO) algorithm, which is one of the most powerful evolutionary algorithms, is used to generate rules for heart disease. First the random rules are encoded and then they are optimized based on their accuracy using PSO algorithm. Finally we compare our results with the C4.5 algorithm.