An Adaptive Neuro-Fuzzy Inference System for Diagnosis of Aphasia

Aphasia is a language disability that has several subdivisions such as Anomic, Broca, Global, and Wernicke. Some reasons such as dissension in description of aphasia and its symptoms, large number of test items which are not quite accurate, linguistic ambiguity and uncertainty as well as typical complexities of medical diagnosis cause accurate diagnosis of aphasia to be a particularly difficult and error prone medical task. To address the diagnosis of the four mentioned common types of aphasia more efficiently, an adaptive Neuro-Fuzzy inference system (ANFIS) is proposed. This structure models the nonlinear relation between aphasia symptoms and resulting test scores as well as the degree of fuzzy belonging of the symptoms to all four major aphasia simultaneously. The proposed method in this paper is compared with a hierarchical fuzzy rule-based structure and a back propagating feed-forward neural network. Our method reaches to a maximum accuracy of 94.6% in 50 trials while the best result for other methods previously used is 91.6%. This method not only diagnoses the four types of aphasia accurately, but also it is efficient for other medical diagnostic applications.