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.