An early prediction model for gestational diabetes mellitus created using machine learning algorithms
The machine-learning-based prediction model effectively identifies populations at high risk for GDM before invasive testing and oral glucose tolerance test, facilitating early clinical intervention and resource optimization.
Authors
- Zhifen Yang (ORCID: https://orcid.org/0000-0003-1464-0140)
- Longbiao Du (ORCID: https://orcid.org/0009-0003-8124-628X)
- Rui Zheng
- Kun Zhang (ORCID: https://orcid.org/0000-0003-4812-7169)
- Jinlong Ma (ORCID: https://orcid.org/0000-0002-4288-6327)
- Xiaoying Zhang (ORCID: https://orcid.org/0000-0001-8057-0764)
- Yongqiang Zhang (ORCID: https://orcid.org/0000-0001-7748-7548)
- Xiaoyue Shi (ORCID: https://orcid.org/0000-0001-7472-400X)
- Shengpu Wang
Institutions
- Hebei Medical University (CN)
- Hebei University of Science and Technology (CN)
- Fourth Hospital of Hebei Medical University (CN)
Publication Details
- Journal
- International Journal of Gynecology & Obstetrics
- Published
- 2025-03-04
- DOI
- https://doi.org/10.1002/ijgo.70055
- Citations
- 4
- Primary Topic
- Gestational Diabetes Research and Management
- Type
- article
- Field-Weighted Citation Impact
- 8.23
Funders
- Natural Science Foundation of Hebei Province