Designing of a New Intuitionistic Fuzzy Based Diabetic Diagnostic System

In this article, fuzzy set theory and other generalized theories are described as having extended the concept of classical set theory to uncertain problems. Fuzzy set theory plays a pivot role in the field of medical sciences to handle the vagueness and impreciseness of the data but no one has explored the field with vague set theory. With this objective, the authors propose a new diabetes diagnosis system called Intuitionistic Fuzzy based Diabetes Diagnostic System (IDDS). IDDS handles the imprecise data of system and helps to predict whether a person is diabetic or not using the concept of Intuitionistic Fuzzy set theory. IDSS uses the Intuitionistic-Fuzzification mechanism which works in two phases. In first phase, it converts the crisp data into intuitionistic fuzzy data and in second phase it generates the fuzzy value for each Intuitionistic value. IDDS has been implemented using MATLAB. The performance of IDDS is evaluated and compared with the Fuzzy Logic Diabetes Diagnosis System (FLDDS). The results prove that IDDS has better performance over FLDDS and it provides more accurate results than FLDDS.