Improving Diagnosis Accuracy of Diabetic Disease Using Radial Basis Function Network and Fuzzy Clustering

Introduction: Nowadays, medical sciences and physicians face a huge amount of data. Diabetes is one of the most expensive glands in the world. Since it is not always easy to diagnose the disease, the physician should examine the outcome of patient tests and decisions made in the past for patients with similar conditions to make an appropriate decision. Due to the large number of patients and the multiple tests performed on each patient, an automated tool for exploring previous patients is needed. Material and Methods: One of the most important methods used to derive data is data mining. Due to the high number of diabetic patients, timely diagnosis and treatment of this disease can reduce the risk of death and its associated medical costs. So far, different systems have been proposed for the diagnosis and prediction of diabetes, but fuzzy logic based systems are used in this study to increase accuracy and efficiency. In the proposed model, fuzzy clustering is first grouped into separate clusters, and then the radial neural network is predicted for each patient with diabetes mellitus. A compatible neuro-fuzzy inference system has also been used to diagnose diabetes. Results: In this paper different classification techniques have been used in MATLAB software to diagnose diabetes mellitus and to classify patients as diabetic and nondiabetic. The dataset used is extracted from the UCI database. The accuracy of the proposed method is 97.14% which is significantly higher than other models of diabetes diagnosis. Conclusion: The application of two fuzzy models has significantly improved the accuracy of diagnosis of diabetes compared to other models proposed in this field.