Construction and performances assessment of the machine learning models
The patients were randomly divided into two groups, of which 80% were used as the training set and the remaining 20% as the testing set. Based on the predictive factors selected, 6 machine learning models were constructed including support vector machine (SVM), K-nearest neighbor (KNN), multi-layer perceptron (MLP), LR, gradient boosting decision tree (GBDT), and adaptive enhancement algorithm (AdaBoost). The models were evaluated and compared by sensitivity, specificity, positive prediction value (PPV), negative prediction value (NPV), accuracy, and the AUC of the ROC.