Data Mining for Predicting Pre-diabetes: Comparing Two Approaches

Abstract

Many individuals who are at risk for type 2 diabetes do not experience symptoms of diabetes, and therefore are not aware of this condition. Screening for type 2 diabetes can identify individuals at risk for type 2 diabetes, and prevent or delay complications. A total of 13 risk factors, out of 17 variables of NHANES', were selected as predictors. In this study, a comparison of two data mining methodology showed that Decision Tree has a higher ROC index than Logistic Regression modeling. All ROC indexes for two data mining models were greater than 77% indicating both methods present a good prediction for pre-diabetes. The final results of comparison indicated Decision Tree modeling is a better indicator to predict pre-diabetes.