A stacked learning framework for accurate classification of polycystic ovary syndrome with advanced data balancing and feature selection techniques
This substantiates its potential as a transformative tool in medical classification. Moreover, beyond immediate applications, this paper explores the generalization of the proposed framework, demonstrating its adaptability and efficacy across different medical classifications. This versatility is exemplified by its successful application to cervical cancer, showcasing the framework potential as a pioneering force in reshaping the landscape of machine-learning applications in healthcare diagnostics.
Authors
- Walid El‐Shafai (ORCID: https://orcid.org/0000-0001-7509-2120)
- Heba M. Emara (ORCID: https://orcid.org/0009-0007-8719-2023)
- Reem Ibrahim Alkanhel (ORCID: https://orcid.org/0000-0001-6395-4723)
- Naglaa F. Soliman (ORCID: https://orcid.org/0000-0001-7322-1857)
- Fathi E. Abd El‐Samie (ORCID: https://orcid.org/0000-0001-8749-9518)
- Abeer D. Algarni (ORCID: https://orcid.org/0000-0001-7199-5082)
Institutions
- Princess Nourah bint Abdulrahman University (SA)
- Prince Sultan University (SA)
- Higher Institute of Engineering (EG)
Publication Details
- Journal
- Frontiers in Physiology
- Published
- 2025-05-06
- DOI
- https://doi.org/10.3389/fphys.2025.1435036
- Citations
- 9
- Primary Topic
- Ovarian function and disorders
- Type
- article
- Field-Weighted Citation Impact
- 15.28