Background: Using random forest to predict arrhythmia after intervention in children with atrial septal defect.
Methods: 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.
Results: Available risk prediction models provided patients with specific risk factor assessments, we used Synthetic Minority Oversampling Technique algorithm and random forest machine learning to propose a prediction model, and got a prediction accuracy of 94.65 % and an Area Under Curve value of 0.8956.
Conclusions: Our study was based on the model constructed by random forest, which can effectively predict the complications of arrhythmia after interventional closure in children with atrial septal defect.
Keywords: Atrial septal defect; Interventional therapy; Random forest; Synthetic Minority Oversampling Technique algorithm.