A Fuzzy Expert System for Prognosis of the Risk of Development of Heart Disease

Fuzzy logic has a high potential for managing the uncertainty sourcesassociated with the medical expert systems. Application of fuzzy inference model hasbeen widely concentrated for managing uncertainties in computer based practices ofmedicine. This paper has proposed two fuzzy expert systems for prognosis of theheart disease based on; 1)Mamdani inference model, and 2) Sugeno inferencemodel. These methods initially received clinical parameters as input sand definetheir corresponding fuzzy sets. The performance of the FESs (Fuzzy Expert System)based on the Mamdani and Sugeno model, have been evaluated using real patientsdataset through conducting two different studies. The dataset includes 380 realcases collected from the Parsian Hospital in Karaj. The accuracy of the proposedMamdani FES is equal to 79.47% and its accuracy using Sugeno model is equal to88.43%. This FES is promising for prognosis of the heart disease and consequentlyearly diagnosis of the disease and improving survival rates.

Keywords:

Fuzzy Inference Model , Fuzzy Expert Systems , Progonsis of the Heart Disease