ArticleBMC pediatrics2025
Developing predictive models for full-term low birth weight infants using ten machine learning algorithms.
Article in BMC pediatrics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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4 authors.
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Abstract
objectiveTerm low birth weight (TLBW) elevates the risk of several health complications and even mortality in infants. By incorporating a range of perinatal factors related to maternal health, we aim to develop predictive models that can enhance clinical decision-making during pregnancy, ultimately promoting the health and well-being of both mothers and newborns.
methodsA retrospective study was conducted on 1,559 singleton term mothers who delivered either TLBW or normal birth weight babies at our hospital between 2019 and 2023. The objective was to identify various perinatal factors associated with maternal characteristics that may contribute to the occurrence of TLBW infants. The cohort was randomly split into training and test sets in a 7:3 ratio. Boruta algorithm, Lasso regression, and logistic regression analyses were applied to identify factors influencing TLBW, with intersections visualized using Venn diagrams. Ten different machine learning algorithms were used to construct predictive models after selecting key influencing factors. Model performance was evaluated using ROC curves, calibration curves, and DCA curves.
resultsSeven key feature variables were identified as contributors to the machine learning model: maternal high-risk factors, low BMI, elevated triglycerides, high albumin levels, elevated homocysteine, low HDL, and high LDL. These factors were recognized as significant risk indicators for the development of TLBW infants. The GBM model, among the ten machine learning algorithms tested, demonstrated exceptional predictive performance. Shapley Additive Explanations (SHAP) were used to interpret the GBM model. Additionally, a web-based risk calculator for predicting TLBW infants was successfully developed using the Shiny framework.
conclusionWe developed a machine learning-based clinical prediction model to identify risk factors associated with TLBW. The model’s performance was thoroughly validated and evaluated, enabling early and accurate detection of TLBW infants. This tool provides valuable support for clinical decision-making and enhances maternal and neonatal care.
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