Datasets | Models | AUC(95% CI) | Sensitivity | Specificity | Accuracy | PPV | NPV |
---|---|---|---|---|---|---|---|
Training | AP model | 0.772(0.701–0.843) | 0.720 | 0.756 | 0.749 | 0.434 | 0.912 |
VP model | 0.722(0.645–0.799) | 0.680 | 0.705 | 0.700 | 0.374 | 0.895 | |
DP model | 0.750(0.679–0.821) | 0.640 | 0.746 | 0.724 | 0.395 | 0.889 | |
AP + VP + DP model | 0.827(0.763–0.891) | 0.760 | 0.798 | 0.790 | 0.494 | 0.928 | |
Clinical model | 0.765(0.687–0.843) | 0.760 | 0.684 | 0.700 | 0.384 | 0.917 | |
Nomogram | 0.894(0.848–0.939) | 0.820 | 0.819 | 0.819 | 0.539 | 0.946 | |
Testing | AP model | 0.760(0.638–0.881) | 0.810 | 0.711 | 0.731 | 0.415 | 0.937 |
VP model | 0.671(0.552–0.790) | 0.952 | 0.349 | 0.471 | 0.270 | 0.967 | |
DP model | 0.668(0.532–0.805) | 0.571 | 0.783 | 0.740 | 0.400 | 0.878 | |
AP + VP + DP model | 0.714(0.604–0.825) | 0.857 | 0.614 | 0.663 | 0.360 | 0.944 | |
Clinical model | 0.783(0.642–0.923) | 0.762 | 0.819 | 0.808 | 0.516 | 0.932 | |
Nomogram | 0.839(0.738–0.940) | 0.868 | 0.867 | 0.827 | 0.560 | 0.911 |