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Using random forest to predict arrhythmia after intervention in children with atrial septal defect. We constructed a prediction model of complications after interventional closure for children with atrial septal defect. The model was based on random forest, and it solved the need for postoperative arrhythmia risk prediction and assisted clinicians and patients' families to make preoperative decisions. Available risk prediction models provided patients with specific risk factor assessments, we used Synthetic Minority Oversampling Technique
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