Using machine learning to predict patients with polycystic ovary disease in Chinese women
Mach-L methods overtook conventional LR and age was the most significant factor, followed by TG, GPT, WBC, UA, and Plt in a cohort of Chinese women.
Authors
- Yao-Jen Liang
- Jyun‐Cheng Ke
- Chun-Kai Wang (ORCID: https://orcid.org/0000-0003-2004-2657)
- Dee Pei (ORCID: https://orcid.org/0000-0003-3080-4248)
- Ta-Wei Chu (ORCID: https://orcid.org/0000-0002-7629-7854)
- Chenyu Wang (ORCID: https://orcid.org/0000-0002-2958-1588)
- Siou-Ting Lee
Institutions
- Fu Jen Catholic University (TW)
- Tri-Service General Hospital (TW)
- Taoyuan Armed Forces General Hospital (TW)
- Kaohsiung Armed Forces General Hospital (TW)
- National Defense Medical Center (TW)
Publication Details
- Journal
- Taiwanese Journal of Obstetrics and Gynecology
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1016/j.tjog.2024.09.019
- Citations
- 4
- Primary Topic
- Ovarian function and disorders
- Type
- article
- Field-Weighted Citation Impact
- 6.79
Funders
- Tri-Service General Hospital