Comparison of Several ANN Architectures on the Thyroid Diseases Grades Diagnosis

Nowadays, with advancement of technology and science and expansion of computer usage in high-tech calculations, especially in the field of medicine, intelligence systems and in particular ANN are becoming of significant importance in automatic diagnosis and prognoses of different diseases. In this article, we have used several ANN architectures (namely RBF, PNN, LVQ) and SVMs, diagnosing thyroid diseases. As the degree of disease development is a critical parameter in medical treatment, we design those networks to classify the grade of diseases, too. The performance of each of them has studied and the best method is selected for each of classification tasks. The overall accuracy of diagnosis system is near 99%.