Table 6.
ROC curve parameters of the 5 machine learning models in the validation set
| Marker | 95% CI | Specificity | Sensitivity | Youden_index | Accuracy | Precision | F1_Score |
|---|---|---|---|---|---|---|---|
| SMV | 0.725-0.810 | 63.28% | 80.10% | 43.38% | 69.96% | 80.10% | 67.93% |
| XGBOOST | 0.817-0.883 | 75.41% | 76.62% | 52.03% | 75.89% | 76.62% | 71.63% |
| GNB | 0.685-0.773 | 59.67% | 75.62% | 35.29% | 66.01% | 75.62% | 63.87% |
| ADABOOST | 0.751-0.830 | 75.08% | 69.65% | 44.73% | 72.92% | 69.65% | 67.15% |
| Random forest | 0.817-0.884 | 76.39% | 77.61% | 54.01% | 76.88% | 77.61% | 72.73% |
Note: SVM, Support Vector Machine; XGBOOST, Extreme Gradient Boosting; GNB, Gaussian Naive Bayes; ADABOOST, Adaptive Boosting.