Table 2.
Results of several performance parameters of machine learning algorithms to predict hemorrhagic transformation in the test dataset.
| TP | FP | FN | TN | Total | Precision | Recall | Accuracy | F1-Score | |
|---|---|---|---|---|---|---|---|---|---|
| BLR | 486 | 28 | 71 | 24 | 609 | 87.3 | 94.6 | 83.7 | 90.8 |
| SVM | 504 | 10 | 78 | 17 | 609 | 86.6 | 98.1 | 85.6 | 92.0 |
| XGB | 486 | 28 | 73 | 22 | 609 | 86.9 | 94.6 | 83.4 | 90.6 |
| ANN_crude | 506 | 17 | 57 | 29 | 609 | 89.9 | 96.7 | 87.8 | 93.2 |
TP: true positive; FP: false positive; FN: false negative; TN: true negative; BLR: binary logistic regression; SVM: support vector machine; XGB: extreme gradient boosting; and ANN crude: artificial neural network crude model.