Abstract
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.
Highlights
Diabetes is one of the most important disorders that is widely spread among people. e disorder is appearing as a result of increasing blood glucose. e pancreas produces insulin that helps blood to carry glucose to all body cells of the human organism
We presented only four membership functions. e learned membership functions are used for describing the antecedent part of type-2 fuzzy rules. e consequent part of the rules uses linear functions characterized by weight coefficients. e type-2 fuzzy neural network (T2FNN) with trained values of c1, c2, o, and w is used for the classification of diabetes in online mode
We presented the results of four simulations—T2FNN with 16, 32, 80, and 100 rules. e designed T2FNN systems with 32, 80, and 100 rules have better accuracy rates than other models. e comparative results obtained demonstrate the efficiency of using the T2FNN system in the diagnosis of diabetes
Summary
Diabetes is one of the most important disorders that is widely spread among people. e disorder is appearing as a result of increasing blood glucose. e pancreas produces insulin that helps blood to carry glucose to all body cells of the human organism. The authors used interval type-2 fuzzy sets for the development of a medical system for the diagnosis of diabetes. Another study presented an ontology model that uses interval type-2 fuzzy sets for the representation of knowledge and for diabetic diet recommendations [39]. Ese two methodologies are integrated for the design of a type-2 fuzzy neural system (T2FNN) for the diagnosis of diabetes As it was shown, a number of research studies have been conducted for the accurate identification of diabetes. Contributions of the paper are the following: the structure of T2FNN that integrates interval type-2 fuzzy sets and neural networks is proposed; the learning algorithm of the system is designed using cross-validation techniques and gradient descent algorithm; and using statistical data and T2FNN structure, a medical diagnostic system is designed for diabetes.
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