Prediction of stillbirth using machine learning methods

This study developed a machine learning model to predict stillbirth using retrospective data from 32,953 singleton pregnancies at multi-centers in South Korea. Variables were collected at baseline, E1 (before 13 weeks of pregnancy), and T0 (before 28 weeks of pregnancy). Each separate cohort (all stillbirths, early stillbirths, late stillbirths) was randomly divided into training and test sets at a 7:3 ratio. Extreme Gradient Boosting Machine algorithm was used to make original models with a full set of variables and simplified models with certain variables from Shapley additive explanations (SHAP) values. The prediction model of whole cohort for all stillbirths achieved an area under the receiver operating characteristic curve (AUC) value of 0.720 (baseline) and 0.740 (E1) with an area under precision-recall curve (AUPR) value of 0.016 (baseline) and 0.019 (E1). The prediction models for early and late stillbirths achieved similar results. For the original model for late stillbirth at T0 achieved AUC of 0.781 and AUPR of 0.015. A simplified model for late stillbirth at T0 using top-ranked SHAP variables demonstrated similar performance with AUC of 0.759 and AUPR of 0.133. Our machine learning model for predicting the late stillbirth in East Asian women in singleton pregnancies with variables before 28 gestational weeks might be helpful for evaluating individual risks.

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Publication Details

Journal
Scientific Reports
Published
2025-10-15
DOI
https://doi.org/10.1038/s41598-025-00335-5
Primary Topic
Pregnancy and preeclampsia studies
Type
article
Field-Weighted Citation Impact
0.00

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article

Prediction of stillbirth using machine learning methods

Min-Joo Lee, Hyun Sun Ko, Su Mi Kim, Jae Eun Shin et al.
Scientific Reports
Pregnancy and preeclampsia studies
article

Prediction of stillbirth using machine learning methods

Min-Joo Lee, Hyun Sun Ko, Su Mi Kim, Jae Eun Shin, Yun Sung Jo, Hanul Hong, Subeen Hong, Oyoung Kim, Woo Jeng Kim, Jeong Ha Wie, Hyun-Soo Choi, Yeon Hee Kim, Sae Kyung Choi, Ki Cheol Kil
article en

Abstract

This study developed a machine learning model to predict stillbirth using retrospective data from 32,953 singleton pregnancies at multi-centers in South Korea. Variables were collected at baseline, E1 (before 13 weeks of pregnancy), and T0 (before 28 weeks of pregnancy). Each separate cohort (all stillbirths, early stillbirths, late stillbirths) was randomly divided into training and test sets at a 7:3 ratio. Extreme Gradient Boosting Machine algorithm was used to make original models with a full set of variables and simplified models with certain variables from Shapley additive explanations (SHAP) values. The prediction model of whole cohort for all stillbirths achieved an area under the receiver operating characteristic curve (AUC) value of 0.720 (baseline) and 0.740 (E1) with an area under precision-recall curve (AUPR) value of 0.016 (baseline) and 0.019 (E1). The prediction models for early and late stillbirths achieved similar results. For the original model for late stillbirth at T0 achieved AUC of 0.781 and AUPR of 0.015. A simplified model for late stillbirth at T0 using top-ranked SHAP variables demonstrated similar performance with AUC of 0.759 and AUPR of 0.133. Our machine learning model for predicting the late stillbirth in East Asian women in singleton pregnancies with variables before 28 gestational weeks might be helpful for evaluating individual risks.

Scientific ReportsVol. 15(1)
Seoul Medical Center (KR), The Catholic University of Korea St. Vincent's Hospital (KR), Daejeon Catholic University (KR), The Catholic University of Korea Incheon St. Mary's Hospital (KR), The Catholic University of Korea Uijeongbu St. Mary's Hospital (KR), The Catholic University of Korea Yeouido St. Mary's Hospital (KR), The Catholic University of Korea Daejeon St. Mary's Hospital (KR), St. Mary's Hospital (US), The Catholic University of Korea Bucheon St. Mary's Hospital (KR), The Catholic University of Korea Seoul St. Mary's Hospital (KR), Catholic University of Korea (KR)
Korea Health Industry Development Institute
Openalex Percentile: Top 64%
Pregnancy and preeclampsia studies
DOIOpen Access PDFOpenAlex
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