Deep Learning Model for Predicting Rhythm Outcomes after Radiofrequency Catheter Ablation in Patients with Atrial Fibrillation

Figure 1

Flowchart of the experimental procedure. Of the 15 factors, 11 were selected using the extreme gradient boosting (XGBoost) algorithm. We used 4-fold cross-validation for model evaluation and applied the synthetic minority oversampling technique to the training set in each fold. ML, machine learning; MLP, multilayer perceptron; PVI, pulmonary vein isolation.