Table 4.
Comparison of the performance of the six models in testing set.
| Model | Testing set | |||||
|---|---|---|---|---|---|---|
| Accuracy | AUC | F1 | Recall | Sensitivity | Specificity | |
| KNN | 0.78 (0.74, 0.81) | 0.82 (0.78, 0.86) | 0.85 (0.82, 0.87) | 0.91 (0.88, 0.94) | 0.91 (0.88, 0.94) | 0.50 (0.42, 0.57) |
| LR | 0.83 (0.80, 0.86) | 0.90 (0.87, 0.93) | 0.88 (0.86, 0.91) | 0.94 (0.92, 0.97) | 0.94 (0.92, 0.97) | 0.61 (0.53, 0.68) |
| NNET | 0.74 (0.70, 0.78) | 0.81 (0.77, 0.85) | 0.82 (0.78, 0.85) | 0.84 (0.80, 0.88) | 0.84 (0.80, 0.88) | 0.53 (0.45, 0.61) |
| RF | 0.84 (0.81, 0.87) | 0.92 (0.89, 0.94) | 0.89 (0.87, 0.91) | 0.95 (0.93, 0.97) | 0.95 (0.93, 0.97) | 0.62 (0.55, 0.69) |
| SVM | 0.82 (0.79, 0.85) | 0.89 (0.86, 0.92) | 0.87 (0.84, 0.89) | 0.86 (0.83, 0.90) | 0.86 (0.83, 0.90) | 0.74 (0.67, 0.81) |
| XGboost | 0.86 (0.83, 0.89) | 0.91 (0.89, 0.94) | 0.90 (0.88, 0.92) | 0.95 (0.92, 0.97) | 0.95 (0.92, 0.97) | 0.68 (0.61, 0.75) |
LR, Logistic regression; RF, Random Forest; XGBoost, Extreme Gradient Boosting; SVC, Support vector Classifier; KNN, k-nearest neighbor; NNET, Neural Network.