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Prediction of stillbirth using machine learning methods.

Woo Jeng Kim1, Sae Kyung Choi1, Yun Sung Jo2

  • 1Department of Obstetrics and Gynecology, Incheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Incheon, Republic of Korea.

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|October 15, 2025
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Summary
This summary is machine-generated.

A machine learning model effectively predicts stillbirth risk in singleton pregnancies using data collected before 28 weeks. This tool aids in evaluating individual risks for expectant mothers, particularly in East Asian populations.

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Area of Science:

  • Perinatal Medicine
  • Artificial Intelligence in Healthcare
  • Reproductive Health Research

Background:

  • Stillbirth remains a significant global health concern, impacting numerous pregnancies annually.
  • Predictive models are crucial for early identification and intervention to reduce stillbirth rates.
  • Existing prediction methods often lack accuracy or are not tailored to specific demographic groups.

Purpose of the Study:

  • To develop and validate a machine learning model for predicting stillbirth risk.
  • To identify key predictive variables using SHAP values for model simplification.
  • To assess the model's performance in a large, multi-center cohort of singleton pregnancies.

Main Methods:

  • Retrospective analysis of 32,953 singleton pregnancies from South Korean multi-centers.
  • Development of Extreme Gradient Boosting Machine models using baseline, E1 (pre-13 weeks), and T0 (pre-28 weeks) data.
  • Validation using Area Under the Curve (AUC) and Area Under Precision-Recall Curve (AUPR) metrics, with model simplification via SHAP values.

Main Results:

  • The model demonstrated good predictive performance for all stillbirths (AUC 0.720-0.740) and late stillbirths (AUC 0.781).
  • A simplified model for late stillbirth achieved comparable performance (AUC 0.759), highlighting key predictive variables.
  • The models showed effectiveness using data available before 28 gestational weeks.

Conclusions:

  • The developed machine learning model offers a promising tool for predicting late stillbirth risk in singleton pregnancies.
  • The model's utility may be particularly relevant for East Asian populations.
  • Early risk assessment using this model could facilitate timely interventions and improve perinatal outcomes.