Table 3.
Performance comparison of six ML models.
| Model | F1 | AUC | Accuracy | Sensitivity | Specificity |
|---|---|---|---|---|---|
| AB | 0.519 | 0.483 | 0.559 | 0.625 | 0.421 |
| LR | 0.568 | 0.617 | 0.576 | 0.525 | 0.684 |
| BAG | 0.579 | 0.624 | 0.610 | 0.650 | 0.526 |
| MLP | 0.601 | 0.680 | 0.627 | 0.650 | 0.579 |
| GBM | 0.482 | 0.497 | 0.525 | 0.600 | 0.368 |
| XGB | 0.515 | 0.511 | 0.542 | 0.575 | 0.474 |
Abbreviation: ML, machine learning; AUC, area under the curve; AB, adaptive boosting; MLP, multilayer perceptron; BAG, bootstrapped aggregating; LR, logistic regression; GBM, gradient boosting machine; XGB, extreme gradient boost.