Table 2.
Performance metrics for five models in the validation dataset.
| Model | AUC (SD) | Accuracy (SD) | Sensitivity (SD) | Specificity (SD) | PPV (SD) | NPV (SD) | F1 score (SD) | Kappa (SD) |
|---|---|---|---|---|---|---|---|---|
| XGBoost | 0.956 (0.089) | 0.887 (0.157) | 0.933 (0.133) | 1.000 (0.000) | 1.000 (0.000) | 0.850 (0.200) | 0.960 (0.080) | 0.790 (0.283) |
| RandomForest | 0.922 (0.097) | 0.887 (0.157) | 0.867 (0.163) | 1.000 (0.000) | 1.000 (0.000) | 0.850 (0.200) | 0.920 (0.098) | 0.790 (0.283) |
| GNB:Gaussian Naive Bayes; | 0.889 (0.141) | 0.860 (0.196) | 0.867 (0.163) | 1.000 (0.000) | 0.900 (0.200) | 0.833 (0.211) | 0.874 (0.170) | 0.723 (0.391) |
| logistic | 0.889 (0.141) | 0.893 (0.137) | 0.933 (0.133) | 0.933 (0.133) | 0.933 (0.133) | 0.867 (0.163) | 0.920 (0.098) | 0.790 (0.273) |
| SVM | 0.889 (0.141) | 0.893 (0.137) | 0.933 (0.133) | 0.933 (0.133) | 0.933 (0.133) | 0.867 (0.163) | 0.920 (0.098) | 0.790 (0.273) |
PPV, Positive Predictive Value; NPV, Negative predictive value; XGBoost, eXtreme Gradient Boosting; SVM, support vector machines; SD, Standard Deviation.