Table 1.
Decision tree model evaluations.
| Classification model | Accuracy (%), mean (SD) | Sensitivity (%; true positive rate), mean (SD) | Specificity (%; true negative rate), mean (SD) | AUCa, mean (SD) |
| Classification and regression decision tree | 70.7 (2.4) | 74 (5.5) | 64 (3.7) | 0.69 (0.001) |
| C5.0 | 56.3 (1.1) | 68.6 (1.9) | 31.1 (6.3) | 0.52 (0.001) |
| Gradient boosting | 71.8 (1.5) | 78.3 (2.8) | 58.7 (4.2) | 0.68 (0.02) |
| Extreme gradient boosting | 65 (4.1) | 77.9 (8.7) | 39.2 (6.6) | 0.62 (0.08) |
| AdaBoost algorithm | 63.6 (3.2) | 73.3 (5.2) | 44 (7.8) | 0.58 (0.05) |
| Random forest | 66.7 (3) | 75 (6.1) | 50 (7.2) | 0.60 (0.01) |
aAUC: area under the receiver operating characteristic curve.