Predictive performance of ML algorithms
diabetes, prediabetes, prediabetes or diabetes
REGRESSION, PREDIABETES, DIABETES
Based on six independent risk factors for HF identified by multivariate logistic regression analysis in the training set, we used five different ML algorithms, including CART, RF, SVM, GBM, and logistic regression, to construct five prediction models for HF risk in prediabetes or diabetes. The predictive performance of five ML models was evaluated in the training set using ROC curve, with the RF model performing best in predicting the risk of HF in the prediabetes or diabetes population (AUC = 0.978), followed by the GBM model (AUC = 0.873), logistic regression model (AUC = 0.870), SVM model (AUC = 0.837), and CART (AUC = 0.822) model (Supporting Information: Figure 