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.

中文翻译：

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一种新颖的模糊推理方法：神经模糊认知图

在这项研究中，提出了一种基于模糊认知图（FCM）和神经模糊推理系统（NFIS）的新方法，称为神经模糊认知图（NFCM）。此处，NFCM用于诊断自身免疫性肝炎（AIH）。AIH是一种慢性炎症性肝病。AIH主要影响女性，通常会对免疫抑制疗法产生临床，生化和组织学缓解。未经治疗的AIH可能导致肝脏瘢痕化并最终导致肝衰竭。如果被快速诊断，AIH通常可以通过药物控制。NFCM是FCM的新扩展，它使用NFIS确定概念之间的因果关系。在建议的方法中，权重是使用专家的知识和经验以及NFIS的优势来计算的。这使提出的模型更加准确。所提出的NFCM模型具有较高的收敛速度，通过实现89.81％的AIH诊断准确性，可以很好地执行。所提出的NFCM模型相对于传统FCM的优势在于，它使用NFIS确定训练系统参数的链路权重。