Recent years, the diabetes mellitus is an important public health problem and has been the top 10 leading causes of death in lower-middle-income countries and upper-middle-income countries in the world. In this study, we tried to use a hybrid feature selection approach to find proper and optimal feature subsets to classify and predict the diabetes mellitus patients in Korea based on the data from Korea National Health and Nutrient Examination Survey (KNHANES). We used the information gain feature selection approach as the filter phase and used the support vector machine with sequential search method as the wrapper phase. To validate the efficiency of the proposed approach, we also compared our proposed approach with several popular feature selection approaches. The results showed that our proposed approach can significantly improve the classification accuracy and outperformed other feature selection approaches.