Early prediction of diabetes using Feature Transformation and hybrid Random Forest Algorithm

Abstract

Diabetes is the most common chronic disease among the world. Early prediction of these will assist the physicians to provide the improved treatment. Machine learning approaches are widely used for predicting the disease at the earlier stage. However the selecting the significant features and the suitable classifier are still reduces the diagnosis accuracy. In this paper the PCA based feature transformation and the hybrid random forest classifier is utilized for diabetes prediction. PCA attempt to identify the best subset of transformed components that greatly improves the classification result. The system is compared with priori machine learning approaches to evaluate the efficiency of this work. The experimental result shows that the present study enhances the prediction accuracy.