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Predicting macrosomia and low birth weight with interpretable machine learning.

Min Cui1, Haiying Yang2, Bingxin Wang2

  • 1Songjiang Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Jiaotong University, Shanghai, 201699, China.

BMC Pregnancy and Childbirth
|February 1, 2026
PubMed
Summary
This summary is machine-generated.

Accurate prediction of abnormal birth weight (macrosomia and low birth weight) is now possible using interpretable machine learning models. These models identify key maternal and fetal factors, improving early risk detection for better perinatal care.

Keywords:
Feature importanceLow birth weightMacrosomiaPerinatal riskPredictive models

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Area of Science:

  • Perinatal Medicine
  • Biostatistics
  • Machine Learning in Healthcare

Background:

  • Abnormal birth weight (macrosomia, low birth weight) presents significant global health challenges.
  • Current prediction methods are limited by fragmented data and lack of interpretability.
  • Traditional statistical models struggle with complex, high-dimensional health data.

Purpose of the Study:

  • To develop interpretable machine learning models for predicting abnormal birth weight.
  • To integrate diverse maternal and fetal characteristics for enhanced prediction accuracy.
  • To facilitate causal inference for understanding risk pathways.

Main Methods:

  • Development and evaluation of 14 machine learning models.
  • Identification of key predictive features using statistical significance and correlation.
  • Causal inference analysis conducted via G-computation.
  • XGBoost model selected for optimal performance.

Main Results:

  • XGBoost achieved high predictive accuracy (AUC 0.997 for macrosomia, 0.992 for low birth weight).
  • Distinct predictive factors identified: metabolic/biometric for macrosomia, placental/hemodynamic for low birth weight.
  • Feature importance analysis revealed specific pathogenic pathways.

Conclusions:

  • Interpretable machine learning offers a robust framework for early abnormal birth weight detection.
  • Model insights support personalized antenatal management strategies.
  • Improved perinatal healthcare outcomes are anticipated through enhanced risk stratification.