An explainable machine learning model in predicting vaginal birth after cesarean section
Objective Vaginal birth after cesarean section (VBAC) is recommended by obstetrical guidelines or expert consensuses. However, no valid tools can exactly predict who can have a vaginal birth among eligible candidates with one prior cesarean section. In recent years, machine learning (ML) is graduall...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-12-01
|
| Series: | The Journal of Maternal-Fetal & Neonatal Medicine |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/14767058.2025.2546544 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849222909236609024 |
|---|---|
| author | Ming Yang Dajian Long Yunxiu Li Xiaozhu Liu Zhi Bai Zhongjun Li |
| author_facet | Ming Yang Dajian Long Yunxiu Li Xiaozhu Liu Zhi Bai Zhongjun Li |
| author_sort | Ming Yang |
| collection | DOAJ |
| description | Objective Vaginal birth after cesarean section (VBAC) is recommended by obstetrical guidelines or expert consensuses. However, no valid tools can exactly predict who can have a vaginal birth among eligible candidates with one prior cesarean section. In recent years, machine learning (ML) is gradually used to develop predictive models in obstetrics and midwifery owing to its excellent performance. This study aimed to develop an explainable ML model to predict the chance of successful VBAC.Methods A total of 2438 pregnant women with trial of labor after cesarean (TOLAC) were analyzed from two tertiary hospitals in Guangdong province of China in the final cohort. The data were collected to establish seven predicting models. Training and internal validation data were collected from the First Dongguan Affiliated Hospital of Guangdong Medical University from January 2012 to December 2022. External validation data were collected from Shenzhen Longhua District Central Hospital from January 2011 to December 2017. Seven predicting models based on ML were developed and evaluated by area under the receiver operating characteristic (AUC) curve. The optimal one was picked out from seven models according to its AUC and other indices. The outcome of the predictive model was interpreted by Shapley Additive exPlanations (SHAP).Results The categorical boosting (CatBoost) model was selected as the predictive model with the greatest AUC for 0.767 (95% CI: 0.685–0.865), the accuracy for 0.652 (95% CI: 0.602–0.713), sensitivity 0.714 (95% CI: 0.576–0.840), and specificity 0.639 (95% CI: 0.574–0.70). Cervical Bishop score and interpregnancy interval showed the greatest impact on successful vaginal birth, according to SHAP results.Conclusions Models based on ML algorithms can be used to predict VBAC. The CatBoost model showed best performance in this study. Based on current evidence-based medical data, clinicians should provide systematic benefit-risk analysis and individualized assessment of VBAC to eligible pregnant women. |
| format | Article |
| id | doaj-art-539e4b36e1f64affa01affd72bb32fe6 |
| institution | Kabale University |
| issn | 1476-7058 1476-4954 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | The Journal of Maternal-Fetal & Neonatal Medicine |
| spelling | doaj-art-539e4b36e1f64affa01affd72bb32fe62025-08-26T02:04:59ZengTaylor & Francis GroupThe Journal of Maternal-Fetal & Neonatal Medicine1476-70581476-49542025-12-0138110.1080/14767058.2025.2546544An explainable machine learning model in predicting vaginal birth after cesarean sectionMing Yang0Dajian Long1Yunxiu Li2Xiaozhu Liu3Zhi Bai4Zhongjun Li5Department of Obstetrics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan, ChinaDepartment of Obstetrics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan, ChinaDepartment of Obstetrics, Shenzhen Longhua District Central Hospital, Shenzhen, ChinaDepartment of Critical Care Medicine, Beijing Shijitan Hospital, Capital Medical University, Beijing, ChinaDepartment of Obstetrics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan, ChinaThe First Clinical Medical School, Guangdong Medical University, Zhanjiang, ChinaObjective Vaginal birth after cesarean section (VBAC) is recommended by obstetrical guidelines or expert consensuses. However, no valid tools can exactly predict who can have a vaginal birth among eligible candidates with one prior cesarean section. In recent years, machine learning (ML) is gradually used to develop predictive models in obstetrics and midwifery owing to its excellent performance. This study aimed to develop an explainable ML model to predict the chance of successful VBAC.Methods A total of 2438 pregnant women with trial of labor after cesarean (TOLAC) were analyzed from two tertiary hospitals in Guangdong province of China in the final cohort. The data were collected to establish seven predicting models. Training and internal validation data were collected from the First Dongguan Affiliated Hospital of Guangdong Medical University from January 2012 to December 2022. External validation data were collected from Shenzhen Longhua District Central Hospital from January 2011 to December 2017. Seven predicting models based on ML were developed and evaluated by area under the receiver operating characteristic (AUC) curve. The optimal one was picked out from seven models according to its AUC and other indices. The outcome of the predictive model was interpreted by Shapley Additive exPlanations (SHAP).Results The categorical boosting (CatBoost) model was selected as the predictive model with the greatest AUC for 0.767 (95% CI: 0.685–0.865), the accuracy for 0.652 (95% CI: 0.602–0.713), sensitivity 0.714 (95% CI: 0.576–0.840), and specificity 0.639 (95% CI: 0.574–0.70). Cervical Bishop score and interpregnancy interval showed the greatest impact on successful vaginal birth, according to SHAP results.Conclusions Models based on ML algorithms can be used to predict VBAC. The CatBoost model showed best performance in this study. Based on current evidence-based medical data, clinicians should provide systematic benefit-risk analysis and individualized assessment of VBAC to eligible pregnant women.https://www.tandfonline.com/doi/10.1080/14767058.2025.2546544Predictive modelsmachine learningvaginal birth after cesarean sectionSHARPCatBoost |
| spellingShingle | Ming Yang Dajian Long Yunxiu Li Xiaozhu Liu Zhi Bai Zhongjun Li An explainable machine learning model in predicting vaginal birth after cesarean section The Journal of Maternal-Fetal & Neonatal Medicine Predictive models machine learning vaginal birth after cesarean section SHARP CatBoost |
| title | An explainable machine learning model in predicting vaginal birth after cesarean section |
| title_full | An explainable machine learning model in predicting vaginal birth after cesarean section |
| title_fullStr | An explainable machine learning model in predicting vaginal birth after cesarean section |
| title_full_unstemmed | An explainable machine learning model in predicting vaginal birth after cesarean section |
| title_short | An explainable machine learning model in predicting vaginal birth after cesarean section |
| title_sort | explainable machine learning model in predicting vaginal birth after cesarean section |
| topic | Predictive models machine learning vaginal birth after cesarean section SHARP CatBoost |
| url | https://www.tandfonline.com/doi/10.1080/14767058.2025.2546544 |
| work_keys_str_mv | AT mingyang anexplainablemachinelearningmodelinpredictingvaginalbirthaftercesareansection AT dajianlong anexplainablemachinelearningmodelinpredictingvaginalbirthaftercesareansection AT yunxiuli anexplainablemachinelearningmodelinpredictingvaginalbirthaftercesareansection AT xiaozhuliu anexplainablemachinelearningmodelinpredictingvaginalbirthaftercesareansection AT zhibai anexplainablemachinelearningmodelinpredictingvaginalbirthaftercesareansection AT zhongjunli anexplainablemachinelearningmodelinpredictingvaginalbirthaftercesareansection AT mingyang explainablemachinelearningmodelinpredictingvaginalbirthaftercesareansection AT dajianlong explainablemachinelearningmodelinpredictingvaginalbirthaftercesareansection AT yunxiuli explainablemachinelearningmodelinpredictingvaginalbirthaftercesareansection AT xiaozhuliu explainablemachinelearningmodelinpredictingvaginalbirthaftercesareansection AT zhibai explainablemachinelearningmodelinpredictingvaginalbirthaftercesareansection AT zhongjunli explainablemachinelearningmodelinpredictingvaginalbirthaftercesareansection |