Research Article
Deep Learning Model for Predicting Rhythm Outcomes after Radiofrequency Catheter Ablation in Patients with Atrial Fibrillation
Table 3
Quantitative evaluation results of evaluated models.
| ā | AUROC | F1 score | Sensitivity | Specificity | Accuracy |
| APPLE score | 0.605 | 0.357 | 0.426 | 0.654 | 0.593 | CHD-VASc score | 0.595 | 0.397 | 0.489 | 0.646 | 0.605 | Linear regression | 0.541 | 0.412 | 0.574 | 0.562 | 0.565 | Logistic regression | 0.546 | 0.381 | 0.511 | 0.585 | 0.565 | XGBoost | 0.608 | 0.452 | 0.617 | 0.600 | 0.605 | SVM | 0.638 | 0.482 | 0.617 | 0.662 | 0.650 | Proposed model | 0.766 | 0.632 | 0.745 | 0.777 | 0.768 |
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The best and second-best results are shown in boldface and italics, respectively.
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