eng Science and Education Publishing Journal of Computer Sciences and Applications 2328-725X 2016-01-04 3 6 181 184 10.12691/jcsa-3-6-14 JCSA20153614 article Tapas Ranjan Baitharu 1 Subhendu Ku. Pani skpani.india@gmail.com 1 Sunil kumar Dhal 2 Department of Computer Science & Engineering, Orissa Engineering College, Bhubaneswar, Odisha (India) under Biju Patnaik University of Technology, Odisha Department of Computer Science & Engineering, SriSri University, Bhubaneswar, Odisha, India 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. http://pubs.sciepub.com/jcsa/3/6/14/jcsa-3-6-14.pdf data mining machine learning algorithms support vector machine kernels function