Medical Expert Systems for Diabetes Diagnosis: A Survey

Medical expert systems being used for disease diagnosis where the patient’s symptoms and other details are inputs and the system diagnose the disease, recommend treatment or drugs which may be prescribed. Medical diagnosis involves several levels of uncertainty and imprecision. This paper reviews the various expert systems used not only for diabetes diagnosis but also gives the treatment and medical advice to patients. The aim of the survey is to determine the current state of research in this area and to help derive the key features and problems with the existing systems. The comparison of the various systems is done on the basis of inputs (data) used for diagnosis, the methodology applied and the platform on which the system is implemented. Most of the diabetic expert system developed diagnoses the Gestational diabetes which is found in pregnant women. The PIMA Indian dataset used for this purpose. Other expert systems used the real time data of patients from hospitals to diagnose the type-2 diabetes. The soft computing methodology is most useful and powerful methodology used for diagnosis purpose. Neural network models, fuzzy system and hybrid-approach such as neuro-fuzzy, ANFIS, PCA are used in many of the