Prediction of arrhythmia after intervention in children with atrial septal defect based on random forest

Hongxiao Sun,Bo Song,Gang Luo,Xiaowen Cui,Silin Pan,Yuhai Liu

doi:10.1186/s12887-021-02744-7

Abstract

BackgroundUsing random forest to predict arrhythmia after intervention in children with atrial septal defect.MethodsWe 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.ResultsAvailable 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.ConclusionsOur 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.

Highlights

Using random forest to predict arrhythmia after intervention in children with atrial septal defect
This paper proposes a random forest (RF) -based risk prediction model for arrhythmia after interventional closure in children with Atrial septal defect (ASD)
Due to the imbalance between the data categories, the Synthetic Minority Oversampling Technique (SMOTE) algorithm is used to classify the data. (Table 2) the data is input into six classifiers to predict postoperative complications, and the prediction performance of the model is evaluated by the leave-oneout method

Summary

Introduction

Using random forest to predict arrhythmia after intervention in children with atrial septal defect. Atrial septal defect (ASD) is the common congenital heart disease (CHD), accounting for about 10 % of the total CHD, including the following four types, primum, secundum, sinus venosus and unroofed coronary sinus types [1,2,3]. The onset of ASD and the occurrence of postoperative arrhythmias should not be ignored, the length of hospital stay (LOS) will increase, which will affect the healthcare system, especially with the current reduction in beds and increasement of costs [6]. Daghistani et al [8] constructed a model for predicting the length of stay of patients with heart disease, and compared artificial neural networks, support vector machines, Bayesian networks and random forest classification algorithms. Based on the random forest model, the prediction performance was the best, the sensitivity, the accuracy and Area

Methods

Findings

Discussion

Conclusion