A Radial Basis Function Neural Network (RBFNN) Approach for Structural Classification of Thyroid Diseases
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The thyroid is a gland that controls key functions of body. Diseases of the thyroid gland can adversely affect nearly every organ in human body. The correct diagnosis of a patient’s thyroid disease clarifies the choice of drug treatment and also allows an accurate assessment of prognosis in many cases. This study investigates Multilayer Perceptron Neural Network (MLPNN) and Radial Basis Function Neural Network (RBFNN) for structural classification of thyroid diseases. A data set for 487 patients having thyroid disease is used to build, train and test the corresponding neural networks. The structural classification of this data set was performed by two expert physicians before the input variables and results were fed into the neural networks. Experimental results show that the predictions of both neural network models are very satisfying for learning data sets. Regarding the evaluation data, the trained RBFNN model outperforms the corresponding MLPNN model. This study demonstrates the strong utility of an artificial neural network model for structural classification of thyroid diseases.
KeywordsRadial Basis Function Neural Network (RBFNN) Multilayer Perceptron Neural Network (MLPNN) Levenberg-Marquardt Structural classification Thyroid
The authors would like to express their respect to the memory of late Dr. Mustafa Kocak who had given the inspiration of this study and thank to Cukurova University Research Hospital personnel for providing medical data for this study.
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