ArticleBMC medical informatics and decision making2024
Exploring parental factors influencing low birth weight on the 2022 CDC natality dataset.
Article in BMC medical informatics and decision making, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Comparison of Johnsons, Inslers formula and ultrasound for estimating fetal weight.Maternal health, neonatology and perinatology · 2026Article
- Factors associated with birth weight and birth length among term newborns in Vietnam: a retrospective cross-sectional study.Child health nursing research · 2026Article
- Determinants of Low Birth Weight in Ghana: Analysis of the 2022 Demographic and Health Survey.Health science reports · 2026Article
- Risk factors for low birthweight among multinational mothers: results from the first population-based maternal and infant health monitoring system in the capital emirate of the United Arab Emirates.BMC pregnancy and childbirth · 2026Article
- Factors associated with preterm birth and low birth weight among infants with congenital heart disease in Changsha City, China, 2022-2024.Scientific reports · 2026Article
- Interpretable machine learning model for predicting low birth weight in singleton pregnancies: a retrospective cohort study.BMC pregnancy and childbirth · 2025Article
- Prediction of birthweight with early and mid-pregnancy antenatal markers utilising machine learning and explainable artificial intelligence.Scientific reports · 2025Article
- Exploratory Evaluation of Circulating Microbiota-Derived Corisin Levels in Women with Adverse Pregnancy Outcomes.Antioxidants (Basel, Switzerland) · 2025Article
- Should the Definition of Low Birth Weight Be Same in Every Ethnicity Considering the DOHaD Concept?Pediatric reports · 2025Article
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Abstract
BACKGROUND AND
aimsLow birth weight (LBW), known as the condition of a newborn weighing less than 2500 g, is a growing concern in the United States (US). Previous studies have identified several contributing factors, but many have analyzed these variables in isolation, limiting their ability to capture the combined influence of multiple factors. Moreover, past research has predominantly focused on maternal health, demographics, and socioeconomic conditions, often neglecting paternal factors such as age, educational level, and ethnicity. Additionally, most studies have utilized localized datasets, which may not reflect the diversity of the US population. To address these gaps, this study leverages machine learning to analyze the 2022 Centers for Disease Control and Prevention's National Natality Dataset, identifying the most significant factors contributing to LBW across the US.
methodsWe combined anthropometric, socioeconomic, maternal, and paternal factors to train logistic regression, random forest, XGBoost, conditional inference tree, and attention mechanism models to predict LBW and normal birth weight (NBW) outcomes. These models were interpreted using odds ratio analysis, feature importance, partial dependence plots (PDP), and Shapley Additive Explanations (SHAP) to identify the factors most strongly associated with LBW.
resultsAcross all five models, the most consistently associated factors with birth weight were maternal height, pre-pregnancy weight, weight gain during pregnancy, and parental ethnicity. Other pregnancy-related factors, such as prenatal visits and avoiding smoking, also significantly influenced birth weight.
conclusionThe relevance of maternal anthropometric factors, pregnancy weight gain, and parental ethnicity can help explain the current differences in LBW and NBW rates among various ethnic groups in the US. Ethnicities with shorter average statures, such as Asians and Hispanics, are more likely to have newborns below the World Health Organization's 2500-gram threshold. Additionally, ethnic groups with historical challenges in accessing nutrition and perinatal care face a higher risk of delivering LBW infants.
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