Integrating SHAP analysis with machine learning to predict postpartum hemorrhage in vaginal births
Abstract Objective This study aimed to develop a machine learning (ML) model integrated with SHapley Additive exPlanations (SHAP) analysis to predict postpartum hemorrhage (PPH) following vaginal deliveries, offering a potential tool for personalized risk assessment and prevention in clinical settin...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-05-01
|
| Series: | BMC Pregnancy and Childbirth |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12884-025-07633-w |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850206062919024640 |
|---|---|
| author | Zixuan Song Hong Lin Mengyuan Shao Xiaoxue Wang Xueting Chen Yangzi Zhou Dandan Zhang |
| author_facet | Zixuan Song Hong Lin Mengyuan Shao Xiaoxue Wang Xueting Chen Yangzi Zhou Dandan Zhang |
| author_sort | Zixuan Song |
| collection | DOAJ |
| description | Abstract Objective This study aimed to develop a machine learning (ML) model integrated with SHapley Additive exPlanations (SHAP) analysis to predict postpartum hemorrhage (PPH) following vaginal deliveries, offering a potential tool for personalized risk assessment and prevention in clinical settings. Methods We conducted a retrospective multicenter cohort study in Northeast China, including women who had vaginal deliveries at three tertiary hospitals from September 2018 to December 2023. Data were extracted from electronic medical records. The dataset was split into a training set (70%) and an internal validation set (30%) to prevent overfitting. External validation was performed on a separate dataset. Several evaluation metrics, including the area under the receiver operating characteristic curve (AUC), were used to compare prediction performance. Features were ranked using SHAP, and the final model was explained. Results The XGBoost model demonstrated superior predictive accuracy for PPH, with an AUC of 0.997 in the training set. SHAP value-based feature selection identified 15 key features contributing to the model’s predictive power. SHAP dependence and summary plots provided intuitive insights into each feature’s contribution, enabling the identification of anomalies. The final model maintained high predictive power, with an AUC of 0.894 in internal validation and 0.880 in external validation. Conclusion This study successfully developed an interpretable ML model that predicts PPH with high accuracy. Future studies with larger and more diverse datasets are necessary to further validate and refine the model, particularly to assess its generalizability across different populations and healthcare settings. |
| format | Article |
| id | doaj-art-e478cd7bbb874c6483d947744a6dd843 |
| institution | OA Journals |
| issn | 1471-2393 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Pregnancy and Childbirth |
| spelling | doaj-art-e478cd7bbb874c6483d947744a6dd8432025-08-20T02:10:56ZengBMCBMC Pregnancy and Childbirth1471-23932025-05-0125111510.1186/s12884-025-07633-wIntegrating SHAP analysis with machine learning to predict postpartum hemorrhage in vaginal birthsZixuan Song0Hong Lin1Mengyuan Shao2Xiaoxue Wang3Xueting Chen4Yangzi Zhou5Dandan Zhang6Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical UniversityDepartment of Obstetrics and Gynecology, Liaoning Maternal and Child Health HospitalDepartment of Obstetrics and Gynecology, Shenyang Women’s and Children’s HospitalDepartment of Health Management, Shengjing Hospital of China Medical UniversityDepartment of Health Management, Shengjing Hospital of China Medical UniversityDepartment of Obstetrics and Gynecology, Shengjing Hospital of China Medical UniversityDepartment of Obstetrics and Gynecology, Shengjing Hospital of China Medical UniversityAbstract Objective This study aimed to develop a machine learning (ML) model integrated with SHapley Additive exPlanations (SHAP) analysis to predict postpartum hemorrhage (PPH) following vaginal deliveries, offering a potential tool for personalized risk assessment and prevention in clinical settings. Methods We conducted a retrospective multicenter cohort study in Northeast China, including women who had vaginal deliveries at three tertiary hospitals from September 2018 to December 2023. Data were extracted from electronic medical records. The dataset was split into a training set (70%) and an internal validation set (30%) to prevent overfitting. External validation was performed on a separate dataset. Several evaluation metrics, including the area under the receiver operating characteristic curve (AUC), were used to compare prediction performance. Features were ranked using SHAP, and the final model was explained. Results The XGBoost model demonstrated superior predictive accuracy for PPH, with an AUC of 0.997 in the training set. SHAP value-based feature selection identified 15 key features contributing to the model’s predictive power. SHAP dependence and summary plots provided intuitive insights into each feature’s contribution, enabling the identification of anomalies. The final model maintained high predictive power, with an AUC of 0.894 in internal validation and 0.880 in external validation. Conclusion This study successfully developed an interpretable ML model that predicts PPH with high accuracy. Future studies with larger and more diverse datasets are necessary to further validate and refine the model, particularly to assess its generalizability across different populations and healthcare settings.https://doi.org/10.1186/s12884-025-07633-wPostpartum hemorrhageVaginal birthsInterpretable modelXGBoostSHAP |
| spellingShingle | Zixuan Song Hong Lin Mengyuan Shao Xiaoxue Wang Xueting Chen Yangzi Zhou Dandan Zhang Integrating SHAP analysis with machine learning to predict postpartum hemorrhage in vaginal births BMC Pregnancy and Childbirth Postpartum hemorrhage Vaginal births Interpretable model XGBoost SHAP |
| title | Integrating SHAP analysis with machine learning to predict postpartum hemorrhage in vaginal births |
| title_full | Integrating SHAP analysis with machine learning to predict postpartum hemorrhage in vaginal births |
| title_fullStr | Integrating SHAP analysis with machine learning to predict postpartum hemorrhage in vaginal births |
| title_full_unstemmed | Integrating SHAP analysis with machine learning to predict postpartum hemorrhage in vaginal births |
| title_short | Integrating SHAP analysis with machine learning to predict postpartum hemorrhage in vaginal births |
| title_sort | integrating shap analysis with machine learning to predict postpartum hemorrhage in vaginal births |
| topic | Postpartum hemorrhage Vaginal births Interpretable model XGBoost SHAP |
| url | https://doi.org/10.1186/s12884-025-07633-w |
| work_keys_str_mv | AT zixuansong integratingshapanalysiswithmachinelearningtopredictpostpartumhemorrhageinvaginalbirths AT honglin integratingshapanalysiswithmachinelearningtopredictpostpartumhemorrhageinvaginalbirths AT mengyuanshao integratingshapanalysiswithmachinelearningtopredictpostpartumhemorrhageinvaginalbirths AT xiaoxuewang integratingshapanalysiswithmachinelearningtopredictpostpartumhemorrhageinvaginalbirths AT xuetingchen integratingshapanalysiswithmachinelearningtopredictpostpartumhemorrhageinvaginalbirths AT yangzizhou integratingshapanalysiswithmachinelearningtopredictpostpartumhemorrhageinvaginalbirths AT dandanzhang integratingshapanalysiswithmachinelearningtopredictpostpartumhemorrhageinvaginalbirths |