Science and Education Publishing Journal of Computer Sciences and Applications 2328-725X 3 6 2016 1 4 Comparison of Kernel Selection for Support Vector Machines Using Diabetes Dataset 181 184 EN Tapas Ranjan Baitharu Subhendu Ku. Pani Department of Computer Science & Engineering, Orissa Engineering College, Bhubaneswar, Odisha (India) under Biju Patnaik University of Technology, Odisha Sunil kumar Dhal JCSA20153614 10.12691/jcsa-3-6-14 2015 7 20 2015 12 11 2016 1 2 One of the major problems in the study of Support vector machine (SVM) is kernel selection, that's based necessarily on the problem of deciding a kernel function for a particular task and dataset. By contradiction to other machine learning algorithms, SVM focuses on maximizing the generalisation ability, which depends on the empirical risk and the complexity of the machine. We were focused on SVM trained using linear, polynomial, puk and Radial Basic Function (RBF) kernels. A preliminary study has been made between SVM using the best choice of kernel. Results had revealed that SVM trained using Linear Kernel is the best choice for dealing with Diabetes dataset.