Determining the risk of gestational diabetes using machine learning: A study on first‐trimester PAPP‐A and β‐hCG data
This study underscores the potential of integrating first-trimester biomarkers with ML algorithms for early GDM prediction. By using routinely collected clinical data, this approach offers a cost-effective and scalable solution for improving maternal and neonatal health outcomes. Future research should validate these findings in diverse populations and explore the incorporation of additional biomarkers to further refine predictive models.
Authors
- Özlem Moraloğlu Tekın (ORCID: https://orcid.org/0000-0001-8167-3837)
- Dilek Şahın (ORCID: https://orcid.org/0000-0001-8567-9048)
- Burak Elmas (ORCID: https://orcid.org/0000-0001-7977-4364)
- Uğurcan Zorlu (ORCID: https://orcid.org/0000-0002-8912-0812)
- Gonca Türker Ergün (ORCID: https://orcid.org/0000-0003-1064-8727)
- Ece Aydoğdu (ORCID: https://orcid.org/0000-0001-9453-9178)
- Serap Topkara Sucu (ORCID: https://orcid.org/0000-0002-9187-2941)
- Elif Ozan (ORCID: https://orcid.org/0009-0008-0761-911X)
Institutions
- Bilkent University (TR)
- Eskişehir City Hospital (TR)
- Memorial Ankara Hospital (TR)
- Başkent University Hospital (TR)
- Ankara Bilkent City Hospital (TR)
Publication Details
- Journal
- International Journal of Gynecology & Obstetrics
- Published
- 2025-06-02
- DOI
- https://doi.org/10.1002/ijgo.70264
- Citations
- 5
- Primary Topic
- Gestational Diabetes Research and Management
- Type
- article
- Field-Weighted Citation Impact
- 10.29