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

<div>Importance of features obtained from extreme gradient boosting algorithm.AF, atrial fibrillation; LV, left ventricular; LVEF, left ventricular ejection fraction; LA, left atrium; eGFR, estimated glomerular filtration rate; HTN, hypertension; AF type, paroxysmal AF vs. persistent AF; DM, diabetes mellitus; HF, heart failure; CAD, coronary artery disease.</div>

Journal of Healthcare Engineering

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Figure 3: Deep Learning Model for Predicting Rhythm Outcomes after Radiofrequency Catheter Ablation in Patients with Atrial Fibrillation 