You are in:Home/Publications/ Expert System Based On Neural-Fuzzy Rules for Thyroid Diseases Diagnosis. International Conference on Bio-Science and Bio-Technology (BSBT 2012), December 16-19, 2012, Korea. Vol. 353 of the Communications in Computer and Information Science series, Springer, pp 94-105. ISBN: 978-3-642-35520-2. DOI: 10.1007/978-3-642-35521-9_13

Dr. Assoc. Prof. Ahmad Taher Azar :: Publications:

Title:
Expert System Based On Neural-Fuzzy Rules for Thyroid Diseases Diagnosis. International Conference on Bio-Science and Bio-Technology (BSBT 2012), December 16-19, 2012, Korea. Vol. 353 of the Communications in Computer and Information Science series, Springer, pp 94-105. ISBN: 978-3-642-35520-2. DOI: 10.1007/978-3-642-35521-9_13
Authors: Ahmad Taher Azar, Aboul Ella Hassanien, Tai-hoon Kim
Year: 2012
Keywords: Not Available
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Local/International: International
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Abstract:

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% and 97.6744% during training and testing phases, respectively with RMSE of 0.02335. After applying feature selection algorithm, LHNFCSF achieved 100% for all cluster sizes during training phase. However, in the testing phase LHNFCSF achieved 88.3721% using one cluster for each class, 90.6977% using two clusters, 91.8605% using three clusters and 97.6744% using four clusters for each class and 12 fuzzy rules. The obtained classification accuracy was very promising with regard to the other classification applications in literature for this problem.

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