Evidence map›Paper›PMID 41184849›Full record

ArticleBMC pregnancy and childbirth2025

Interpretable machine learning model for predicting low birth weight in singleton pregnancies: a retrospective cohort study.

Xiaojuan Wu, Qingxiang Zhao, Yong Gao, Yiyu Zhang, Linrui Xu, Xianzhu Cong, Na Sun, Fuyan Shi, Suzhen Wang

Abstract read
In one paragraph

Article in BMC pregnancy and childbirth, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Xiaojuan WuDepartment of Health Statistics, School of Public Health, Shandong Second Medical University, Weifang, China.
Qingxiang ZhaoDepartment of Pain Management, Binzhou Medical University Hospital, Binzhou, China.
Yong GaoDepartment of Pain Management, Binzhou Medical University Hospital, Binzhou, China.
Yiyu ZhangDepartment of Health Statistics, School of Public Health, Shandong Second Medical University, Weifang, China.
Linrui XuDepartment of Health Statistics, School of Public Health, Shandong Second Medical University, Weifang, China.
Xianzhu CongDepartment of Health Statistics, School of Public Health, Shandong Second Medical University, Weifang, China.
Na SunDepartment of Health Statistics, School of Public Health, Shandong Second Medical University, Weifang, China.
Fuyan ShiDepartment of Health Statistics, School of Public Health, Shandong Second Medical University, Weifang, China. shifuyan@sdsmu.edu.cn.
Suzhen WangDepartment of Health Statistics, School of Public Health, Shandong Second Medical University, Weifang, China. wangsz@sdsmu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundLow birth weight (LBW), defined as a newborn weighing less than 2500 g, is an increasingly significant public health concern. Exploring the risk and protective factors for LBW is getting more and more important. This study aimed to utilize predictive models to identify the critical factors associated with LBW in singleton pregnancies.

methodsA retrospective cohort study was conducted at the Binzhou Medical University Hospital, China, from 2022 to 2023. Singleton pregnancies with gestational age exceeding 27 weeks were included, while multiple pregnancies and fetal anomalies were excluded. Logistic regression (LR) model and four machine learning (ML) algorithms were tested(random forest, light gradient boosting machine, support vector machine, and extreme gradient boosting). The LR model was interpreted through odds ratio analysis and clinical nomogram visualization. Shapley Additive Explanations (SHAP) analysis was used to interpret the importance and impact of individual features on ML model.

resultsIn this cohort of 10,227 deliveries, 237 cases were classified as LBW. The XGBoost model exhibited superior performance in predicting LBW, with an AUC of 0.786 (training set) and 0.741 (test set, ) AUPRC of 0.350 and 0.186, respectively. Both LR and XGBoost model identified maternal age, gestational age, BMI, hypertensive disorders of pregnancy (HDP), fetal distress as the critical factor. Additionally, a follow-up study on LBW found that LBW infants prone to significant health challenges, such as a high rate of hospitalization and the complex conditions including congenital anomalies, neonatal respiratory distress syndrome (NRDS) and neonatal hyperbilirubinemia.

conclusionThis study demonstrated that the LR and XGBoost model exhibited clinically meaningful predictive performance in identifying factors associated with LBW in singleton pregnancies. Pregnant women with a gestational age of less than 37 weeks, a gestational BMI below 18 kg/m², maternal age under than 25 years, and maternal comorbidities such as HDP or fetal distress are at higher risk of delivering LBW infants.

Indexed as

Infant, Low Birth WeightMachine LearningAdultChinaFemaleGestational AgeHumansInfant, NewbornLogistic ModelsPregnancyRetrospective StudiesRisk FactorsLow birth weightMachine learningNeonatal complicationsNomogramShapley additive explanations

Identifiers

PMID41184849
PMCPMC12581232

What Socratic holds

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.