Table 5. Evaluation performance of the original ACS prediction model before/after preprocessing.
| Model | Accuracy | AUC | Precision | Recall | F1 score |
|---|---|---|---|---|---|
| Logistic Regression | 96.9 | 79.8 | 66.3 | 54.3 | 56.4 |
| Random Forest | 97.2 | 78.1 | 86.1 | 52.3 | 56.3 |
| Gradient Boosting | 96.5 | 78.9 | 59.8 | 54.1 | 55.6 |
| XGBoost | 97.2 | 80.3 | 79.9 | 53.7 | 56.1 |
| Deep Neural Network | 96.1 | 68.5 | 57.0 | 53.9 | 54.9 |
| 1D-CNN | 97.1 | 58.3 | 70.9 | 52.9 | 54.6 |
| Logistic Regression | 96.1 | 87.1 | 79.5 | 72.8 | 75.7 |
| Random Forest | 97.1 | 90.7 | 94.5 | 71.5 | 78.6 |
| Gradient Boosting | 97.1 | 89.3 | 89.4 | 75.2 | 80.6 |
| XGBoost | 96.9 | 89.8 | 90.6 | 70.4 | 76.8 |
| Deep Neural Network | 95.1 | 80.3 | 72.4 | 69.5 | 70.8 |
| 1D-CNN | 96.5 | 76.8 | 82.5 | 73.9 | 77.5 |