The Combination of Fuzzy Cognitive Map and Possibilistic Fuzzy C-Means Algorithm for Grading Celiac Disease

In this paper a new method based on fuzzy cognitive map (FCM) and possibilistic fuzzy c-means (PFCM) clustering algorithm for categorizing celiac disease (CD) will be presented. CD is a chronic disease and a certain immunologically determined form of enteropathy, which affects small intestine of the adults and children who are genetically predisposed. This method incorporates membership and possibility to classify each patient by combining the fuzzy c-mean and possibilistic c-means (PCM). We use both fuzzy memberships and possibilistic typicalities to model the uncertainty implied in the data sets. Fuzzy c-mean and PCM are the two most well-known clustering algorithms in fuzzy clustering area. Recently there have been several attempts to combine both of them. In this research, 89 cases are studied. Three experts extracted seven main determinant characteristics of CD which were considered as FCM concepts. The mutual effects of these concepts on one another and on the final concept were expressed in the form of fuzzy rules and linguistic variables. Ultimately, combining the FCM model with PFCM algorithm, we obtained the grade A, B1, and B2 accuracies as 88%, 90%, and 91% respectively.

The Combination of Fuzzy Cognitive Map and Possibilistic Fuzzy C-Means Algorithm for Grading Celiac Disease Keywords:

Celiac disease , fuzzy cognitive map possibilistic fuzzy c-means