Type-2 Fuzzy Neural System for Diagnosis of Diabetes

Diabetes is a chronic disease that is characterized by insufficient production or utilization of insulin and a consequent high increase in blood sugar. Diagnosis of diabetes is a complex process and requires a high level of expertise. The disease is characterized by a set of signs and symptoms. Some of these symptoms are obtained through laboratory analysis. Creation of a knowledge base and automation of disease diagnosis are important and allow fast detection and treatment. Various techniques have been used to develop a high-accuracy system for the diagnosis of diabetes. Fuzzy logic is one of the appropriate methodologies for the development of such medical diagnostic systems. Several research studies have used fuzzy models to diagnose medical diseases due to the imprecision and uncertainty associated with medical data. Moreover, a high level of uncertainty in medical data requires a type-2 fuzzy system to handle these uncertainties and diagnose diabetes. The paper proposes the integration of a type-2 fuzzy system and neural networks for the diagnosis of diabetes. Using the structure of type-2 fuzzy neural network (T2FNN) and statistical data, the system’s design for the diagnosis of diabetes is performed. A number of simulations have been done in order to evaluate the performance of the designed system. The comparative results demonstrated the efficiency of using the T2FNN system in the diagnosis of diabetes. The physician can use the system for diabetes’ diagnosis.