Table 6.
Evaluation criteria for comparison performance of machine learning models (LR, RF, SVM and k-NN)
| Evaluation criteria | Model | |||
|---|---|---|---|---|
| variables | RF | LR | SVM | K-NN |
| Accuracy | 0.922 | 0.876 | 0.901 | 0.887 |
| Sensitivity | 0.851 | 0.675 | 0.733 | 0.804 |
| Specificity | 0.944 | 0.918 | 0.909 | 0.923 |
| Positive predictive value | 0.722 | 0.783 | 0.701 | 0.688 |
| Negative predictive value | 0.884 | 0.849 | 0.803 | 0.794 |
| AUC | 0.905 | 0.827 | 0.851 | 0.883 |
RF: Random forest; LR: Logistic regression; SVM: Support vector machine; k-NN: k- nearest neighbor; AUC: area under the curve of mean receiver operating characteristics