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

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Abstract

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.

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Most cited references 21

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SMOTE: Synthetic Minority Over-sampling Technique

N. Chawla, K. W. Bowyer, L Hall … (2002)

An approach to the construction of classifiers from imbalanced datasets is described. A dataset is imbalanced if the classification categories are not approximately equally represented. Often real-world data sets are predominately composed of ``normal'' examples with only a small percentage of ``abnormal'' or ``interesting'' examples. It is also the case that the cost of misclassifying an abnormal (interesting) example as a normal example is often much higher than the cost of the reverse error. Under-sampling of the majority (normal) class has been proposed as a good means of increasing the sensitivity of a classifier to the minority class. This paper shows that a combination of our method of over-sampling the minority (abnormal) class and under-sampling the majority (normal) class can achieve better classifier performance (in ROC space) than only under-sampling the majority class. This paper also shows that a combination of our method of over-sampling the minority class and under-sampling the majority class can achieve better classifier performance (in ROC space) than varying the loss ratios in Ripper or class priors in Naive Bayes. Our method of over-sampling the minority class involves creating synthetic minority class examples. Experiments are performed using C4.5, Ripper and a Naive Bayes classifier. The method is evaluated using the area under the Receiver Operating Characteristic curve (AUC) and the ROC convex hull strategy.

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Deep Learning in Medical Imaging: General Overview

June-Goo Lee, Sanghoon Jun, Young-Won Cho … (2017)

The artificial neural network (ANN)–a machine learning technique inspired by the human neuronal synapse system–was introduced in the 1950s. However, the ANN was previously limited in its ability to solve actual problems, due to the vanishing gradient and overfitting problems with training of deep architecture, lack of computing power, and primarily the absence of sufficient data to train the computer system. Interest in this concept has lately resurfaced, due to the availability of big data, enhanced computing power with the current graphics processing units, and novel algorithms to train the deep neural network. Recent studies on this technology suggest its potentially to perform better than humans in some visual and auditory recognition tasks, which may portend its applications in medicine and healthcare, especially in medical imaging, in the foreseeable future. This review article offers perspectives on the history, development, and applications of deep learning technology, particularly regarding its applications in medical imaging.

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Racial and temporal variations in the prevalence of heart defects.

Kate L. Erickson, L Botto, Juan A. Correa (2001)

Documenting the prevalence and trends of congenital heart defects provides useful data for pediatric practice, health-care planning, and causal research. Yet, most population-based studies use data from the 1970s and 1980s. We sought to extend into more recent years the study of temporal and racial variations of heart defects occurrence in a well-defined population. We used data from the Metropolitan Atlanta Congenital Defects Program, a population-based registry with active case ascertainment from multiple sources. Heart defects were identified among liveborn infants up to 1 year old, among stillborn infants, and among pregnancy terminations to mothers residing in metropolitan Atlanta. From 1968 through 1997, the registry ascertained 5813 major congenital heart defects among 937 195 infants, for a prevalence of 6.2 per 1000. The prevalence increased to 9.0 per 1000 births in 1995 through 1997. The prevalence of ventricular septal defects, tetralogy of Fallot, atrioventricular septal defects, and pulmonary stenosis increased, whereas that of transposition of the great arteries decreased. For some defects, prevalence and trends varied by race. The prevalence of congenital heart defects is increasing. Whereas most findings likely result from improved case ascertainment and reporting, others might be because of changes in the distribution of risk factors in the population. The basis of the racial variations is incompletely understood.

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Author and article information

Contributors

Bo Song: 13789888999@163.com

Silin Pan: silinpan@126.com

Journal

Journal ID (nlm-ta): BMC Pediatr

Journal ID (iso-abbrev): BMC Pediatr

Title: BMC Pediatrics

Publisher: BioMed Central (London )

ISSN (Electronic): 1471-2431

Publication date (Electronic): 16 June 2021

Publication date PMC-release: 16 June 2021

Publication date Collection: 2021

Volume: 21

Electronic Location Identifier: 280

Affiliations

[1 ]GRID grid.410645.2, ISNI 0000 0001 0455 0905, Qingdao Women and Children’s Hospital, , Qingdao University, ; 266034 Qingdao, China

[2 ]GRID grid.9227.e, ISNI 0000000119573309, Institute Oceanology, , Chinese Academy of Sciences, ; 266071 Qingdao, China

[3 ]GRID grid.412610.0, ISNI 0000 0001 2229 7077, Qingdao University of Science and Technology, ; 266061 Qingdao, China

Article

Publisher ID: 2744

DOI: 10.1186/s12887-021-02744-7

PMC ID: 8207618

PubMed ID: 34134641

SO-VID: 8434b631-43ae-4ded-9315-624c54827471

License:

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

History

Date received : 17 February 2021

Date accepted : 27 May 2021

Funding

Funded by: National Natural Science Foundation of China

Award ID: 81770316

Funded by: Qingdao Science and Technology Plan

Award ID: 20-3-4-47-nsh

Funded by: Shandong Taishan Scholarship

Award ID: 2018

Custom metadata

ScienceOpen disciplines: Pediatrics

Keywords: atrial septal defect,interventional therapy,random forest,synthetic minority oversampling technique algorithm

Data availability:

ScienceOpen disciplines: Pediatrics

Keywords: atrial septal defect, interventional therapy, random forest, synthetic minority oversampling technique algorithm

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

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Abstract

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Conclusions

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Estudio del peso al nacer

Most cited references 21

SMOTE: Synthetic Minority Over-sampling Technique

Deep Learning in Medical Imaging: General Overview

Racial and temporal variations in the prevalence of heart defects.

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