Fuzzy C-Means Clustering Interval Type-2 Cerebellar Model Articulation Neural Network for Medical Data Classification

Abstract

This paper presents a fuzzy c-means clustering interval type-2 cerebellar model articulation neural network (FCM-IT2CMANN) method to help physicians improve diagnostic accuracy. The proposed method combines two classifiers, in which the IT2CMANN is the primary classifier and the fuzzy c-means algorithm is the pre-classifier. First, the data are divided into nc groups using the pre-classifier, and then, the main classifier is applied to determine whether the sample is in a healthy or diseased state. Implementing the gradient descent method, the adaptive laws for updating the FCM-IT2CMANN parameters are derived. Furthermore, the system convergence is proven by the Lyapunov stability theory. Finally, the classification of breast cancer and liver disease datasets from the University of California at Irvine is conducted to illustrate the effectiveness of the proposed classifier.