Development and validation of prediction models
prediabetes or diabetes
REGRESSION
By using weighted univariate and multivariate logistic regression, our study identified separate risk factors for the risk of HF in individuals with prediabetes or diabetes. Following that, ML prediction models were created using the variables chosen by stepwise regression as input variables. The ratio of 7:3 was used to randomly divide all participants into a training set for building the prediction model and a validation set for validating the model. On the training set, models were built using five ML algorithms, including logistic regression (LR), random forest (RF), classification and regression tree (CART), gradient boosting machine (GBM), and support vector machine (SVM). The performance of each model is validated and compared in the validation set. The receiver operating characteristic (ROC) curve of the five ML models were plotted in the training and validation sets, and the area under the ROC curve (AUC) were calculated to evaluate the predictive power of the five ML models. The model with the largest AUC was selected as the best model for each algorithm. Calibration curve was used to compare the degree of calibration of each model. Decision curve analysis (DCA) was used to analyze and compare them for clinical applications. To confirm whether the model is overfitting, internal model validation is performed using Bootstrap method.