Diagnosis of gastrointestinal disorders using DIAGNET

A new neural network model called DIAGNET is proposed in this paper for diagnosing gastrointestinal disorders. DIAGNET is a combination of Backpropagation neural network (BPNN) and radial basis functions neural network (RBFNN). 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 the DIAGNET and trained for diagnosing the diseases related to gastrointestinal disorders. The trained model is tested with new patient’s symptoms and signs. The performance of the DIAGNET is compared with the existing Backpropagation neural network and Radial basis functions neural network models. Sensitivity, Specificity and Receiver-Operating Characteristics (ROC) are used as the indicators for testing the accuracy of the models which predict the gastrointestinal disorder diseases. The results suggest that the DIAGNET can be better solution for complex, nonlinear medical decision support systems.