Table 2.
Prediction of Different Machine Learning Models and Logistic Regression in the Validation Dataset (n = 61)
| LR | SVM | RF | |
|---|---|---|---|
| AUC (95% CI) | 0.543 (0.389–0.697) | 0.653 (0.505–0.802) | 0.422 (0.27–0.575) |
| Accuracy (95% CI) | 0.537 (0.374–0.693) | 0.659 (0.494–0.799) | 0.415 (0.263–0.579) |
| Balanced Accuracy | 0.543 | 0.653 | 0.422 |
| Sensitivity | 0.455 | 0.727 | 0.318 |
| Specificity | 0.632 | 0.579 | 0.526 |
| Positive predictive value | 0.588 | 0.667 | 0.438 |
| Negative predictive value | 0.5 | 0.647 | 0.4 |
| Prevalence | 0.537 | 0.537 | 0.537 |
| Precision | 0.588 | 0.667 | 0.438 |
| Recall | 0.455 | 0.727 | 0.318 |
| F1 Score | 0.513 | 0.696 | 0.368 |
| Kappa | 0.085 | 0.308 | −0.152 |
Abbreviations: AUC, area under receiver operating characteristic curve; CI, confidence interval; LR, logistic regression; SVM, support vector machine; RF, random forest.