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A DECISION SUPPORT SYSTEM FOR PREDICTION OF THYROID DISEASE-A COMPARISON OF MULTILAYER PERCEPTRON NEURAL NETWORK AND RADIAL BASIS FUNCTION NEURAL NETWORK

Thyroid disease is a major cause of concern in medical diagnosis and the prediction / onset of which is a difficult proposition in medical research. In this research, we use two Neural Network models Multilayer perceptron (MLP) and Radial Basis Function Networks (RBF) for the prediction of onset of thyroid disease using the data generated in real life. The MLP is trained and tested with Back-propagation algorithm whereas RBF networks was trained and tested with SPSS software. Thyroid disease database which had been published and was used for empirical comparisons and the results show that MLP and RBF show the almost same kind of results in diagnosing the thyroid disease.