An investigation of neuro-fuzzy systems in psychosomatic disorders

A neuro-fuzzy model for diagnosis of psychosomatic disorders is proposed in this paper. The symptoms and signs are collected from the patients through oral interview. For the linguistic nature of patient's inputs, an artificial domain is created and fuzzy membership values are defined. The fuzzy values are fed as inputs to feedforward multilayer neural network. The network is trained using Backpropagation training algorithm. The trained model is tested with new patient's symptoms and signs. Further, the performance of the diagnosing capability is compared with medical expert. The performance of the model is also compared with probability model based on Bayesian Belief Network and statistical model using Linear Discriminant analysis