Fig. 1

Model training, parameter adjustment and performance evaluation. 551 patients were recruited in the current study. The data were pre-processed and randomly divided into a training set (80%) and a validation set (20%), and the proportion of the two class proportions in each set is the same. In the training set, k-fold cross-validation (k = 10) is used, and various parameter combinations are exhausted by grid search. Performance evaluation indices such as AUC and AP were adopted to judge the average predictive performance of the model. The average performance maximum is used as the best performance tuning parameter, and the prediction is finally performed on the test set