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Objective <p>Term 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.</p> Methods <p>A 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.</p> Results <p>Seven 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.</p> Conclusion <p>We 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.</p>

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Developing predictive models for full-term low birth weight infants using ten machine learning algorithms

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月17日

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  • Limin Chen,
  • Hui Shao,
  • Jianwei Zhang,
  • Ruoya Wu

摘要

Objective

Term 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.

Methods

A 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.

Results

Seven 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.

Conclusion

We 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.