Evaluation of the predictive model
The ROC curve and AUC was used to evaluate the discrimination of the predictive model, with an AUC range of 0-1, where 1 indicates complete consistency and 0.5 indicates poor consistency. The Hosmer-Lemeshow test and calibration plot were used to calibrate the predictive model and judge the consistency between the predicted probability and the observed probability. When P>0.05 for the Hosmer-Lemeshow test, it can be considered that the predictive model has good calibration.The calibration plot is a commonly used visual tool for assessing the consistency between model-predicted probabilities and actual observed outcomes. In the calibration plot, the x-axis represents the predicted probabilities from the model, while the y-axis represents the frequency or probabilities of the observed outcomes. The predicted results refer to the model’s outputs obtained by predicting new, unseen samples based on input features and learned parameters. On the other hand, the observed outcomes represent the known class labels of the actual observed samples, which are used to compare the accuracy and effectiveness of the model’s predictions.