In the current world millions of people are suffering with bone diseases such as osteoporosis and osteopenia. The early detection of osteoporosis and osteopenia disease is very important as it helps people to be cautious and get treated on time. Hence research on early detection of osteoporosis and osteopenia disease has gained importance across the world. In this paper, analysis of suitable kernel for Support Vector Machine (SVM) focussing on the classification of osteoporosis and osteopenia disease has been carried out and presented. The kernel functions considered includes polynomial, linear, RBF and Gaussian to find the optimal one for the classification of osteoporosis and osteopenia disease with improved accuracy.