Diagnosis of diabetes by using Adaptive SVM and feature selection

In this study, a new Support Vector Machine (SVM) based method for diagnosis of diabetes is proposed. In the proposed method, feature of adaptibility is added to the support vector machine. Thus, a new kind of SVM named “Adaptive SVM” is proposed, and by using it together with the Feature Selection Method, smartly diagnosis of diseases is aimed. During the training and testing of this newly designed smart system, diabetes data set which is obtained from the medical database of University of California is used. It is observed that classification rate of this newly proposed method on the diabetes daha set is more successful than the similar studies which are implemented so far and which are in the literature.