APPLICATION OF THE RELEVANCE VECTOR MACHINE AND SUPPORT VECTOR MACHINE TO CLINICAL DATA

The model is applied to cancer, tumor , and general health diseases . The results obtained using RVM are compared with those of state-of-the-art Support Vector Machine (SVM) to present the advantages of RVMs over SVMs. The finding results allow us to conclude that RVM is almost equal to SVM on training efficiency and classification accuracy, but RVM performs better on sparse property, generalization ability, and decision speed.