Generating Weights for Fuzzy Decision Making Mechanism to Diagnose Heart Disease

Abstract

Abstract
Heart disease is one of the diseases spread around the world. It suddenly kills the human community. So use of fuzzy logic to diagnosis the heart disease is essential. So the study was conducted with the following components. They are fuzzification, fuzzy decision making mechanism and defuzzification. The crisp values are changed into fuzzy values by fuzzification. Fuzzy decision making mechanism is based on adaptive neuro fuzzy inference system which has five layers. In layer 1 the rules are generated with the weights. The weights for each rule are derived by using S weights. The output parameters are also predicted by fuzzy predicted value. The fuzzy values from fuzzy decision making mechanism are transferred into crisp values by defuzzification. With the crisp values the doctors and patients can diagnose the heart disease. The proposed algorithm was tested with Cleveland heart disease dataset. The proposed algorithm was implemented using MATLAB fuzzy logic tool box and it
works more effectively than the earlier methods.

Keywords: Fuzzy decision making mechanism, rules, S weight, fuzzy predicted value, heart disease

Cite this Article
Senthil Kumar AV. Generating Weights for Fuzzy Decision Making Mechanism to Diagnose Heart Disease. Journal of Computer Technology & Application. 2015; 6(2): 7–13p.