Improvements Of A Similarity Measure With Their Applications In Medical Diagnostic System

Similarity measure between IFSs has become one of the most significant issues in the field of IFS, several studies on similarity measures have been proposed in the literature. However, some of the similarity measures exist counter-intuitive cases,which can’t meet all the objective conditions. In 2014, Boran and Akay from Gazi University in Turkey proposed a new biparametric similarity measure on intuitionistic fuzzy sets which improved the failure of some pattern recognitions in the mainstream method. Furthermore, on the basis of these findings, Boran and Akay imported an isosceles right triangle area, which is corresponding to the values of the membership degree and the non-membership degree. However the distance measure function only applies to the most special condition— the midline of the hypotenuse. In this paper,three new general type of similarity measures for IFSs are proposed, which involve the whole area of the triangle, and as a result, the new formulas can be used in a wider space.In the latter part of the article, we design a medical diagnostic system, which plays a supporting role in the diagnosis. According to symptoms of the disease, we get the patients’ data. Then we organize the data into a intuitionistic fuzzy set. At last, we calculate the intuitionistic fuzzy set with the standard model of disease. We can get the degree from the calculation, which uses the intuitionistic fuzzy sets similarity measure. If the similarity degree between the two intuitionistic fuzzy sets is very large,we can draw the conclusion that the patient has a great probability of getting the disease. The patient needs to get a detailed examination. Otherwise, we can draw the conclusion that the patient has not got the disease. In addition, the diagnose system makes a comparison between the original formula and the three new formulas.According to the data displayed through the system, we can clearly see the superiority of the improved formula in the diagnosis of disease. This system provides a new light on the development of medical diagnostic system,which aims at not only reducing the doctors’ workload but also improving the real-time diagnosis and the diagnosticaccuracy.