Comparison between multiple logistic regression and machine learning methods in prediction of abnormal thallium scans in type 2 diabetes

Figure 1 Flowchart of sample selection from the Cardinal Tien Hospital Diabetes Study Cohort.

Figure 2 Proposed scheme for four machine learning methods. CART: Classification and regression tree; RF: random forest; XGBoost: eXtreme gradient boosting; NB: Naïve Byes.

Figure 3 Integrated importance ranking of all risk factors.

Figure 4 Receiver operation curve of logistic regression and other four different machine learning methods. LGR: Logistic regression; CART: Classification and regression tree; RF: Random forest; XGBoost: eXtreme gradient boosting; NB: Naïve Byes.