Prediction of preterm birth using machine learning: a comprehensive analysis based on large-scale preschool children survey data in Shenzhen of China
Our study found that ML models, particularly XGBoost, show promise in accurately predicting PTB and identifying key risk factors. These findings provide the potential of ML for enhancing clinical interventions, personalizing prenatal care, and informing public health initiatives.
Authors
- Guo‐Min Wen
- Weikang Yang (ORCID: https://orcid.org/0000-0001-8366-8943)
- Dan‐Xia Xian
- Maolin Zhang (ORCID: https://orcid.org/0000-0002-3388-4442)
- Weiqing Chen (ORCID: https://orcid.org/0000-0002-3877-0794)
- Yafen Zhao
- Deng‐Li Sun
- Xiaona Yin
- Liwen Ding (ORCID: https://orcid.org/0000-0001-6411-1875)
Institutions
- Sun Yat-sen University (CN)
- Shenzhen Children's Hospital (CN)
Publication Details
- Journal
- BMC Pregnancy and Childbirth
- Published
- 2024-12-04
- DOI
- https://doi.org/10.1186/s12884-024-06980-4
- Citations
- 8
- Primary Topic
- Preterm Birth and Chorioamnionitis
- Type
- article
- Field-Weighted Citation Impact
- 3.09