Table 2.
Comparison of model performance predicting OSA risk in different machine learning techniques.
| LR | RF | SVM | XGBoost | |
|---|---|---|---|---|
| AUROC (%) | 97.2 | 96.0 | 91.6 | 96.7 |
| (95% CI) | (93.9–99.5) | (91.7–99.2) | (85.7–96.8) | (92.4–99.5) |
| AUPRC (%) | 97.0 | 95.3 | 91.9 | 95.7 |
| (95% CI) | (93.7–99.5) | (91.0–99.1) | (85.1–96.8) | (92.4–99.5) |
| Sensitivity (%) | 93.0 | 97.7 | 90.7 | 95.3 |
| (95% CI) | (85.9–97.6) | (84.5–96.2) | (75.9–90.9) | (84.6–96.3) |
| Specificity (%) | 90.7 | 83.7 | 76.7 | 86.0 |
| (95% CI) | (85.1–97.6) | (84.9–96.3) | (75.7–91.6) | (84.2–96.4) |
| Accuracy (%) | 91.9 | 90.7 | 83.7 | 90.7 |
| (95% CI) | (86.0–97.6) | (84.8–96.3) | (75.4–90.7) | (84.5–96.4) |
| PPV (%) | 90.9 | 85.7 | 79.6 | 87.2 |
| (95% CI) | (85.3–97.1) | (84.6–96.3) | (75.6–90.8) | (84.4–96.5) |
| NPV (%) | 92.9 | 97.3 | 89.2 | 94.9 |
| (95% CI) | (85.3–96.8) | (84.4–96.4) | (76.1–91.1) | (84.5–96.5) |
| F1 score (%) | 92.0 | 91.3 | 84.8 | 91.1 |
| (95% CI) | (85.5–96.8) | (84.3–96.0) | (76.0–91.1) | (83.8–96.4) |
| Threshold | 0.547 | 0.308 | 0.298 | 0.396 |
The F1 score is calculated by 2 × sensitivity × PPV/(sensitivity + PPV). AUROC = area under receiver operating characteristic, AUPRC = area under the precision–recall curve, CI = confidence interval, LR = logistic regression, NPV = negative predictive value, PPV = positive predictive value, RF = random forest, RUS = random undersampling, SVM = support vector machine, XGBoost = extreme gradient boosting.