A Novel Fuzzy Inference Approach: Neuro-fuzzy Cognitive Map

Abstract

In this study, a new approach based on fuzzy cognitive map (FCM) and neuro-fuzzy inference system (NFIS), called the neuro-fuzzy cognitive map (NFCM), is proposed. Here, the NFCM is used for diagnosis of autoimmune hepatitis (AIH). AIH is a chronic inflammatory liver disease. AIH primarily affects women and typically responds to immunosuppressive therapy with clinical, biochemical, and histological remission. An untreated AIH can lead to scarring of the liver and ultimately to liver failure. If rapidly diagnosed, AIH can often be controlled by medication. NFCM is a new extension of FCM, which employs a NFIS to determine the causal relationships between concepts. In the proposed approach, weights are calculated using the knowledge and experience of experts as well as the advantages of NFIS. This makes the presented model more accurate. Having a high convergence speed, the proposed NFCM model performs well by achieving an AIH diagnosis accuracy of 89.81%. The superiority of the proposed NFCM model over the conventional FCM is that, it uses the NFIS to determine the link weights which train system parameters.