Table 2 Comparisons on accuracy, precision, recall, specificity and F-score of classification among different machine learning approaches
Performance metric | NB | SVM | RF | XGBoost | Boosting | KNN | LASSO |
|---|---|---|---|---|---|---|---|
ROC-AUC | 0.653 | 0.805 | 0.803 | 0.854 | 0.858 | 0.866 | 0.868 |
Accuracy, % | 59.76 | 80.49 | 80.57 | 77.02 | 77.97 | 79.10 | 79.44 |
Precision, % | 93.52 | 85.47 | 84.73 | 80.80 | 84.00 | 83.28 | 85.53 |
Recall, % | 34.02 | 80.56 | 81.74 | 79.97 | 77.32 | 80.71 | 78.35 |
Specificity, % | 96.62 | 80.38 | 78.90 | 72.78 | 78.90 | 76.79 | 81.01 |
F-score, % | 49.89 | 82.94 | 83.21 | 80.38 | 80.52 | 81.97 | 81.78 |