Evaluation of SVM Kernels for Health Risks Assessment

As per the statistics of the National Family Health Survey - 4 (2015-16) only 28.7% of families in India have been covered under Health Insurance. There are many categories of the population that are not covered by the insurance companies as they are reluctant to insure them. The main reason is that they are not able to compute fitness level of the people who wants to get insured properly. In this paper, the preliminary analysis of data set is given equal importance as in constructing and fine-tuning machine learning model. The selection of features for machine learning models was done based on correlation as well as on the medical significance of the attributes. The features that are medically significant and has a minimum correlation among themselves were selected for constructing SVM kernel models. The selection of the most appropriate SVM Kernel was done by multiple evaluations of six SVM kernels. It was found that the Medium Radial kernel performs best in term of accuracy but linear kernel training time is least among all kernels. The values of C-statistics are consistent with the accuracy values in almost all cases and it shows that medium radial kernel is the best choice to automate the health risk assessment as it is faster as well as most accurate in prediction.