Operational definitions | AUROC | AUPRC | F1 score | Precision | Recall | Accuracy | Specificity |
---|---|---|---|---|---|---|---|
Rule-based method | 91.47 | 73.39 | 68.68 | 54.80 | 91.98 | 91.47 | 91.41 |
Statistical models or tree-based machine learning techniques | |||||||
Logistic | 90.76 | 88.90 | 86.39 | 84.45 | 88.43 | 86.06 | 83.68 |
Random Forest | 92.70 | 91.70 | 79.03 | 92.55 | 68.96 | 81.68 | 94.43 |
XGBoost | 94.46 | 92.80 | 91.50 | 91.16 | 91.83 | 91.45 | 91.08 |
Neural network-based deep learning techniques: with embedding | |||||||
MLP | 98.00 | 98.00 | 94.82 | 95.41 | 94.24 | 94.85 | 95.45 |
LSTM | 100.00 | 100.00 | 99.04 | 99.21 | 98.78 | 99.04 | 99.21 |
GRU | 100.00 | 100.00 | 99.81 | 99.92 | 99.69 | 99.81 | 99.93 |
CNN | 100.00 | 100.00 | 99.55 | 99.46 | 99.64 | 99.55 | 99.46 |