Generating Weights for Fuzzy Decision Making Mechanism to Diagnose Heart Disease

Authors

  • A. V. Senthil Kumar PG & Research, Department of Computer Application, Hindusthan College of Arts & Science, Coimbatore, Tamil Nadu, India

Keywords:

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

Abstract

Heart disease is one of the diseases spread around the world. It suddenly kills the humancommunity. So use of fuzzy logic to diagnosis the heart disease is essential. So the study wasconducted with the following components. They are fuzzification, fuzzy decision makingmechanism and defuzzification. The crisp values are changed into fuzzy values byfuzzification. Fuzzy decision making mechanism is based on adaptive neuro fuzzy inferencesystem which has five layers. In layer 1 the rules are generated with the weights. The weightsfor each rule are derived by using S weights. The output parameters are also predicted byfuzzy predicted value. The fuzzy values from fuzzy decision making mechanism are transferredinto crisp values by defuzzification. With the crisp values the doctors and patients candiagnose the heart disease. The proposed algorithm was tested with Cleveland heart diseasedataset. The proposed algorithm was implemented using MATLAB fuzzy logic tool box and itworks more effectively than the earlier methods. Keywords: Fuzzy decision making mechanism, rules, S weight, fuzzy predicted value, heart diseaseCite 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.

Author Biography

  • A. V. Senthil Kumar, PG & Research, Department of Computer Application, Hindusthan College of Arts & Science, Coimbatore, Tamil Nadu, India
    DirectorDepartment of MCA 

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Published

2015-05-26

Issue

Vol. 6 No. 2 (2015)

Section

Research Articles

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