Conclusions
RECURRENCE, PAROXYSMAL ATRIAL FIBRILLATION
This study showed that the explainable machine learning model can effectively reveal its decision-making process in identifying patients with paroxysmal atrial fibrillation at high risk for recurrence after catheter ablation by listing important features, showing the impact of every feature on model output, determining appropriate thresholds and identifying significant outliers. Physicians could clearly see the unreasonable judgements in model prediction, to combine model outputs and clinical experience and assist in decision-making. Studies based on high-dimensional databases with large sample size are necessary to further confirm the universality of this finding.