Fuzzy rule-based expert system for diagnosis of thyroid disease

The diseases in glands of human bodies have been increased with high rate in the last decade. Thyroid is one of these glands that its disease has spread worldwide. The main function of thyroid gland is to balance the metabolism and cells´ activity. Since it does its own task abnormally, thyroid disorders will occur and the negligence of them may cause irreparable events. Because of inaccessibility of Endocrinologist experts for most people, modeling and developing an expert system for diagnosis thyroid´s disease that can be accessible in everywhere is vital. This paper presents a fuzzy rule-based expert system for diagnosis thyroid´s disease. This proposed system includes three steps: pre-processing (feature selection), neuro-fuzzy classification and system evaluating. In the proposed system, the process of diagnosis encounters with vagueness and uncertainty in final decision. So, we handled the imprecise knowledge by using fuzzy logic. In neuro-fuzzy classification step, we generated initial fuzzy rules by k-means algorithm and then scaled conjugate gradient algorithm (SCG) was used to determine the optimum values of parameters. In the last step, we used the generated fuzzy rules to model and evaluate the system. This system can help non-experts who are suspicious of their thyroid function or it can be used as a diagnosis assistance system to help experts for assuring their diagnosis.