An explainable machine learning model in predicting vaginal birth after cesarean section
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.
Authors
- Da-Jian Long
- Yunxiu Li (ORCID: https://orcid.org/0000-0002-8180-374X)
- Ming Yang (ORCID: https://orcid.org/0000-0002-1715-3850)
- Zhongjun Li (ORCID: https://orcid.org/0000-0002-6022-5215)
- Xiaozhu Liu (ORCID: https://orcid.org/0000-0001-7419-3480)
- Zhi Bai
Institutions
- Guangdong Medical College (CN)
- Capital Medical University (CN)
- Third Affiliated Hospital of Southern Medical University (CN)
- Longgang Central Hospital (CN)
- First Affiliated Hospital of Guangzhou Medical University (CN)
- Dongguan People’s Hospital (CN)
- Beijing Shijitan Hospital (CN)
- Guangzhou Medical University (CN)
Publication Details
- Journal
- The Journal of Maternal-Fetal & Neonatal Medicine
- Published
- 2025-08-25
- DOI
- https://doi.org/10.1080/14767058.2025.2546544
- Primary Topic
- Maternal and Perinatal Health Interventions
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Basic and Applied Basic Research Foundation of Guangdong Province