Novel binary logistic regression model based on feature transformation of XGBoost for type 2 Diabetes Mellitus prediction in healthcare systems

Abstract

The rapidly increasing incidence of Diabetes Mellitus (DM) has shown that DM is a serious disease that endangered human life in all parts of the world. The late stage of Type-II DM (T2DM) in particular is accompanied by complex complications. Healthcare systems with various data mining algorithms can help the endocrinologist to find whether patients have diabetes in the early detection of T2DM. In the present research, a novel and efficient binary logistic regression (BLR) is proposed founding on feature transformation of XGBoost (XGBoost-BLR) for accurately predicting the specific type of T2DM, and making the model adaptive to more than one dataset. In order to raise the identification ratio, the databases are executed by series of preprocessing procedures which include removing outliers, normalization, and missing value processing. We select features that have a more significant effect on the results by χ2 test (CST). Then, the selected features are projected into high-dimensional feature space by XGBoost. Finally, the high-dimensional features generated can be modeled by the BLR application. The proposed XGBoost-BLR achieved a 94% and 98% identification rate for diabetes prediction in Pima Indians Diabetes Database (PIDD) and Early-Stage Diabetes Risk Prediction Database (ESDRPD).