Novel Approach Of Diabetes Disease Classification By Support Vector Machine With RBF Kernel

Early diagnosis of any disease with less cost is always preferable. Diabetes is one such disease. It has become the fourth leading cause of death in developed countries and is also reaching epidemic proportions in many developing and newly industrialized nations. Diabetes leads to increase in the risks of developing kidney disease, blindness, nerve damage, blood vessel damage and heart disease also. In this research work, Support Vector Machine with RBF Kernel is used for finding out the classification accuracy of diabetes dataset. In the given method, the advance algorithm of SVM-RBF kernel is used; it contains some of the extended parameters for feature selection as well as the proposed correlation with SVM method obtains on UCI dataset. In this work, investigation is done on automatic approach to diagnose diabetes disease based on Support vector machine with RBF kernel and MLP (Multilayer perceptrons). The concept of data mining is used, in which the proposed SVM-RBF method obtains 88% accuracy on UCI diabetes dataset, which is better than other models. Keywords— Diabetes, Nmachine Learning, Svm, Feature Selection.