Predicting the risk of threatened abortion using machine learning methods: a comparative study
Our research on constructing a prediction model for threatened abortion through routine blood tests has revealed the great potential of ML algorithms in detecting threatened abortion. This algorithm is expected to analyse routine blood data to identify at-risk pregnancies at an early stage, significantly improving the early detection of this common pregnancy complication. It will assist healthcare providers in intervening earlier and reducing the incidence of abortion. However, before the model can be translated into routine clinical applications, more extensive validation studies are still needed.
Authors
- Jie Su (ORCID: https://orcid.org/0000-0003-0534-3793)
- Junjie Guo (ORCID: https://orcid.org/0000-0002-3414-3734)
- Na Wei (ORCID: https://orcid.org/0000-0002-8803-3303)
- Zhi Zhu (ORCID: https://orcid.org/0000-0001-6655-5189)
- Shiyu Wu (ORCID: https://orcid.org/0000-0002-8809-8326)
- C. X. Yue (ORCID: https://orcid.org/0000-0001-6783-7647)
- Chao Chen (ORCID: https://orcid.org/0000-0002-4476-4366)
- Biao Song
- Zicheng Zhang (ORCID: https://orcid.org/0009-0005-5419-5071)
Institutions
- Inner Mongolia University of Finance and Economics (CN)
- Beijing Institute of Big Data Research (CN)
- Inner Mongolia Electric Power Survey & Design Institute (China) (CN)
- Goldwind (China) (CN)
- Affiliated Hospital of Shaanxi University of Chinese Medicine (CN)
- Inner Mongolia Medical University (CN)
- Shaanxi University of Chinese Medicine (CN)
Publication Details
- Journal
- BMC Pregnancy and Childbirth
- Published
- 2025-08-30
- DOI
- https://doi.org/10.1186/s12884-025-08030-z
- Citations
- 1
- Primary Topic
- Reproductive Health and Contraception
- Type
- article
- Field-Weighted Citation Impact
- 1.88