Table 4.
Results of performance evaluation of machine learning models
| Row | Classifier | Accuracy | Sensitivity | Specificity | F-measure | AUC | Mean |
|---|---|---|---|---|---|---|---|
| 1 | XGBClassifier | 90.70 | 95.00 | 86.96 | 90.48 | 90.98 | 90.82 |
| 2 | HistGradientBoostingClassifier | 90.70 | 90.00 | 91.30 | 90.00 | 90.65 | 90.53 |
| - | Average 1 and 2 | 90.70 | 92.50 | 89.13 | 90.24 | 90.82 | - |
| 3 | AdaBoostClassifier | 86.05 | 75.00 | 95.65 | 83.33 | 85.33 | 85.07 |
| 4 | RandomForestClassifier | 83.72 | 80.00 | 86.96 | 82.05 | 83.48 | 83.24 |
AUC: Area under the curve, XGBClassifier: eXtreme Gradient Boosting classifier