A Hybrid Intelligent Framework for Thyroid Diagnosis

Thyroid disease exists across the whole world and many people are suffering from this disease. The diagnosis of thyroid disease is of great importance to human life. Although there are already some researches that introduces various methods for thyroid diagnosis and achieves good results, the performance of diagnosis still needs to be improved. Therefore, a hybrid intelligent framework, in which an optimal support vector machine (SVM) based on a hybrid optimization algorithm and a recursive feature elimination (RFE) method are incorporated, is proposed to predict thyroid disease in this paper. The hybrid optimization algorithm combines the teaching-learning based algorithm (TLBO) and differential evolution (DE), contributing to the parameter optimization of SVM. And the RFE method is introduced to obtain the optimal feature subsets for thyroid diagnosis. A thyroid dataset collected from UCI repository is utilized to evaluate the performance of the proposed framework. The experimental results demonstrate that the proposed framework achieves better and more stable performance than other compared methods.