Development and validation of an explainable machine learning and nomogram model for early detection and risk stratification of polycystic ovary syndrome: a multicenter study
This interpretable "XGBoost + SHAP" and nomogram framework provides an accurate, transparent, and practical tool for early PCOS screening and individualized management.
Authors
- Xingyu Yu (ORCID: https://orcid.org/0009-0009-5382-973X)
- Jiayan Chen (ORCID: https://orcid.org/0000-0002-8209-7218)
- Cheng Zhang (ORCID: https://orcid.org/0000-0002-8277-5138)
- Jijun Tong
- Yunyan Zhang (ORCID: https://orcid.org/0000-0001-7440-5420)
- Bihua Yao
- Xiaotong Zhu (ORCID: https://orcid.org/0000-0003-2085-3011)
Institutions
- Zhejiang Chinese Medical University (CN)
- Zhejiang Sci-Tech University (CN)
- Wenzhou Medical University (CN)
- Zhejiang Lab (CN)
- First Hospital of Jiaxing (CN)
Publication Details
- Journal
- Frontiers in Endocrinology
- Published
- 2025-12-17
- DOI
- https://doi.org/10.3389/fendo.2025.1719631
- Primary Topic
- Ovarian function and disorders
- Type
- article
- Field-Weighted Citation Impact
- 0.00