Table 7. Performance of four machine learning algorithms in the binary classification framework.
| Model |
Accuracy |
Precision
(PPV) |
Recall
(Sensitivity) |
Specificity |
NPV |
F1 |
AUC |
Model |
| SVM |
0.73 |
0.72 |
0.73 |
0.73 |
0.73 |
0.72 |
0.65 |
0.73 |
| RF |
0.74 |
0.74 |
0.74 |
0.74 |
0.74 |
0.71 |
0.72 |
0.74 |
| XGBoost |
0.74 |
0.73 |
0.74 |
0.74 |
0.74 |
0.73 |
0.72 |
0.74 |
| Adaboost |
0.75 |
0.74 |
0.75 |
0.75 |
0.75 |
0.73 |
0.77 |
0.75 |
PPV: Positive Predictive Value; NPV: Negative Predictive Value. Under the balanced test set (1:1 class ratio), PPV is equivalent to Precision, and Specificity is mathematically equivalent to NPV in this specific context; NPV values were derived from the confusion matrices of the independent test set.
