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On-line Access: 2022-05-13

Received: 2021-09-07

Revision Accepted: 2021-12-26

Crosschecked: 2022-05-13

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Clicked: 299

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Danqing CHEN

https://orcid.org/0000-0002-0201-7215

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Journal of Zhejiang University SCIENCE B 2022 Vol.23 No.5 P.432-436

http://doi.org/10.1631/jzus.B2100753


Prediction of birth weight in pregnancy with gestational diabetes mellitus using an artificial neural network


Author(s):  Menglin ZHOU, Jiansheng JI, Ni XIE, Danqing CHEN

Affiliation(s):  Department of Obstetrics, Women’s Hospital, Zhejiang University School of Medicine, Hangzhou 310006, China; more

Corresponding email(s):   chendq@zju.edu.cn

Key Words:  Gestational diabetes mellitus, Birth weight, Prediction, Artificial neural network


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Menglin ZHOU, Jiansheng JI, Ni XIE, Danqing CHEN. Prediction of birth weight in pregnancy with gestational diabetes mellitus using an artificial neural network[J]. Journal of Zhejiang University Science B, 2022, 23(5): 432-436.

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Abstract: 
gestational diabetes mellitus (GDM) is common during pregnancy, with the prevalence reaching as high as 31.0% in some European regions (McIntyre et al., 2019). Dysfunction of the glucose metabolism in pregnancy can influence fetal growth via alteration of the intrauterine environment, resulting in an increased risk of abnormal offspring birth weight (McIntyre et al., 2019). Infants with abnormal birth weight will be faced with increased risks of neonatal complications in the perinatal period and chronic non-communicable diseases in childhood and adulthood (Mitanchez et al., 2015; McIntyre et al., 2019). Therefore, accurate estimation of birth weight for neonates from women with GDM is crucial for more sensible perinatal decision-making and improvement of perinatal outcomes. Timely antenatal intervention, with reference to accurately estimated fetal weight, may also decrease the risks of adverse long-term diseases.

应用人工神经网络预测妊娠期糖尿病新生儿出生体重

目的:建立一个预测妊娠期糖尿病新生儿出生体重的人工神经网络模型,并评估其预测的准确性。
创新点:妊娠期糖尿病新生儿出生体重的预测十分重要,但目前预测精度欠佳。本研究利用大样本量的临床数据,突破传统统计学方法,应用机器学习建立了一个基于人工神经网络的预测模型,其预测精度较传统方法有明显提升。
方法:收集2462名妊娠期糖尿病孕妇的临床数据,其中80%的数据用于构建一个前馈神经网络模型,并用反向传播算法和10折交叉验证法训练和优化;剩余20%的数据用于验证最终模型的性能,并将其与传统方法进行比较。
结论:本研究构建的人工神经网络模型对妊娠期糖尿病新生儿出生体重具有较高的预测精度,其预测能力优于传统方法,不足之处则在于其仍有可能会低估高出生体重。

关键词:妊娠期糖尿病;出生体重;预测;人工神经网络

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