Diagnosis of Diabetes using Correlation fuzzy logic in Fuzzy Expert System

M. Kalpana, Dr. A.V Senthil Kumar

Abstract


Fuzzy expert system framework constructs large scale knowledge based system effectively for diabetes. Fuzzy Expert System helps the medical practitioners to solve decision problem. The components of correlation fuzzy determination mechanism are determination logic and knowledge base. The fuzzification interface converts the crisp values into fuzzy values for the diagnosis of diabetes. The determination logic evaluates the effect on the number of membership functions, the shape of membership functions and the effect of fuzzy operators. Correlation fuzzy logic is computed for fuzzy numbers and membership function. Knowledge base is constructed by fuzzy if-then rules. Defuzzification interface converts the resulting fuzzy set into crisp values. The result of the proposed method is compared with earlier method using accuracy as metrics. The proposed fuzzy expert system can work more effectively for diabetes application and also improves the accuracy of fuzzy expert system.

Keywords: Fuzzy Expert System, Correlation Fuzzy Determination Mechanism, determination logic, knowledge base, Diabetes application.


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DOI: https://doi.org/10.26483/ijarcs.v3i1.998

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