Research Article
Using Machine Learning Algorithms to Predict Hepatitis B Surface Antigen Seroclearance
Table 3
Summary of predictive performance of each model.
| | Model | TP | FN | TN | FP | Precision | Sensitivity | F-score | AUC (95% CI) |
| | Logistic regression | 0 | 35 | 636 | 0 | 1.00 | 0.95 | 0.97 | 0.680 (0.677, 0.683) | | Decision tree | 4 | 31 | 627 | 9 | 0.97 | 0.94 | 0.95 | 0.619 (0.614, 0.624) | | Random forest | 4 | 31 | 635 | 1 | 0.99 | 0.95 | 0.97 | 0.829 (0.824, 0.834) | | Extreme gradient boosting | 9 | 26 | 632 | 4 | 0.98 | 0.96 | 0.97 | 0.891 (0.889, 0.895) |
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