Enhancing early gestational diabetes mellitus prediction with imputation-based machine learning framework: A comparative study on real-world clinical records
This study demonstrates the critical role of imputation in improving the performance and fairness of GDM prediction models. The findings provide practical guidance for integrating imputation into clinical machine learning pipelines.
Authors
- Liangkun Ma (ORCID: https://orcid.org/0000-0001-5758-0198)
- Ziyang Wang (ORCID: https://orcid.org/0000-0002-0368-9590)
- Lin Yang (ORCID: https://orcid.org/0000-0001-9211-0079)
- Jie Hao (ORCID: https://orcid.org/0009-0002-7208-7443)
- Leyao Ma (ORCID: https://orcid.org/0000-0003-2645-6754)
- Jiao Li (ORCID: https://orcid.org/0000-0001-6391-8343)
- Mingyue Hu
- Y Li (ORCID: https://orcid.org/0009-0009-6816-614X)
- Yaxin Wang (ORCID: https://orcid.org/0000-0002-8801-9237)
- Yin Sun (ORCID: https://orcid.org/0000-0003-2903-5410)
- Suhan Zhang (ORCID: https://orcid.org/0000-0002-6691-6201)
- Ye Li
Institutions
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- Peking Union Medical College Hospital (CN)
- Obstetrics and Gynecology Hospital of Fudan University (CN)
Publication Details
- Journal
- Digital Health
- Published
- 2025-05-01
- DOI
- https://doi.org/10.1177/20552076251352436
- Citations
- 3
- Primary Topic
- Gestational Diabetes Research and Management
- Type
- article
- Field-Weighted Citation Impact
- 6.17