A novel soft computing method based on interval type-2 fuzzy logic for classification of celiac disease

This study proposes a new method for automatic classification of medical data on celiac disease (CD) using an interval type-2 fuzzy logic system (IT2FLS). Due to the high uncertainty in the medical data, IT2FLSs are able to consider many linguistic uncertainties in the type-2 framework, and considering the uncertainty in the membership functions, they can raise the accuracy of the fuzzy system. To improve the performance of IT2FLS, we have used fuzzy C-means (FCM) clustering algorithm to determine the membership functions centers in the fuzzy rules. For the purpose of comparison, other classification models based on fuzzy sets, such as type-1 fuzzy logic system (T1FLS) and IT2FLS without using FCM are also proposed. Experiments are performed on a dataset of Poursina Hakim Research Institute with the real samples of patients with different grades of celiac. Accuracy of 83.33%, 87.88% and 90.65%, respectively, was achieved when determining the grades of A, B1 and B2 by IT2FLS-FCM. This demonstrates the superiority of this model over the other fuzzy models. Considering the uncertainty in type-2 fuzzy sets and as well as FCM clustering algorithm, improved system performance in the classification of CD.