Expert System Based On Neural-Fuzzy Rules for Thyroid Diseases Diagnosis
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The thyroid, an endocrine gland that secretes hormones in the blood, circulates its products to all tissues of the body, where they control vital functions in every cell. Normal levels of thyroid hormone help the brain, heart, intestines, muscles and reproductive system function normally. Thyroid hormones control the metabolism of the body. Abnormalities of thyroid function are usually related to production of too little thyroid hormone (hypothyroidism) or production of too much thyroid hormone (hyperthyroidism). Therefore, the correct diagnosis of these diseases is very important topic. In this study, Linguistic Hedges Neural-Fuzzy Classifier with Selected Features (LHNFCSF) is presented for diagnosis of thyroid diseases. The performance evaluation of this system is estimated by using classification accuracy and k-fold cross-validation. The results indicated that the classification accuracy without feature selection was 98.6047 phases, respectively with RMSE of 0.02335. After applying feature selection algorithm, LHNFCSF achieved 100 However, in the testing phase LHNFCSF achieved 88.3721 each class, 90.6977 97.6744 classification accuracy was very promising with regard to the other classification applications in literature for this problem.
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