Expert System Based On Neural-Fuzzy Rules for Thyroid Diseases Diagnosis
  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.
