A GA Approach for Tuning Membership Functions of a Fuzzy Expert System for Heart Disease Prognosis Development Risk

Application of soft computing hybrid models have been concentrated to cope with uncertainty in the medical expert systems, recently. Heart disease is one of the mortal diseases that can be controlled in early stages. In this paper a hybrid Fuzzy-GA model for the Heart Disease Prediction (HDP) problem has been proposed. For this, first a Fuzzy Expert System (FES) using Mamdani model was presented. Then the membership functions parameters of the FES were optimized using the hybrid Fuzzy-Genetic Algorithm (Fuzzy-GA). The reason of selecting fuzzy method was its high potential to address the uncertainty sources in the knowledge of medical experts. Performance of the FES and Fuzzy-GA model were evaluated using a real dataset of 380 patients collected from Parsian Hospital in Karaj, Iran. Accuracy of the designed FES before optimization was 85.52%. After optimization using the hybrid Fuzzy-GA, the accuracy of this system was increased to 92.37%. The proposed hybrid model competes with its counterparts in terms of interpretability and accuracy in prognosis process of the heart disease. This model is promising for early diagnosis of the heart disease and saving more people lives.