Table 3.
Overfitting evaluation of the prediction models.
| Models | AUC [95%CI] | P-value | |
|---|---|---|---|
| Training cohort | Test cohort | ||
| LR | 0.886 [0.817–0.936] | 0.845 [0.732–0.924] | 0.4426 |
| SVM | 0.845 [0.769–0.904] | 0.829 [0.713–0.912] | 0.8556 |
| DT | 0.832 [0.754–0.893] | 0.827 [0.711–0.911] | 0.7253 |
| RF | 0.812 [0.769–0.904] | 0.843 [0.729–0.922] | 0.5767 |
| XGBoost | 0.858 [0.784–0.914] | 0.836 [0721–0.917] | 0.7146 |
| Clinical model | 0.730 [0.642–0.807] | 0.805 [0.686–0.894] | 0.2854 |
| Combined model | 0.921 [0.858–0.962] | 0.865 [0.757–0.938] | 0.3245 |
P-value reflected the differences between the training and test cohorts, and P < 0.05 (two-sided) were considered statistically significant.
AUC area under the curve; CI confidence interval; LR logistic regression; SVM support vector machine; DT decision tree; RF random forest; XGBoost extreme gradient boosting.