ORIGINAL RESEARCH article
Front. Physiol.
Sec. Computational Physiology and Medicine
Volume 16 - 2025 | doi: 10.3389/fphys.2025.1435036
A Stacked Learning Framework for Accurate Classification of Polycystic Ovary Syndrome with Advanced Data Balancing and Feature Selection Techniques
Provisionally accepted
Heba M Emara1*
Walid El-Shafai1
Naglaa F. Soliman2Abeer D. Algarni2Reem Alkanhel1,2Fathi E. Abd El-Samie2- 1University of Menoufia, Shibin Al Kawm, Al Minufiyah, Egypt
- 2Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
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In the domain of women's health, the intricate conditions of Poly-Cystic Ovary Syndrome (PCOS) demand sophisticated methodologies for accurate identification and intervention. This study introduces an innovative machinelearning framework tailored to precisely classify instances of PCOS. The methodology incorporates stacked learning and depends on the Adaptive Synthetic (ADASYN) algorithm, Synthetic Minority Over-sampling Technique (SMOTE), and random oversampling methods for addressing data imbalances. The BORUTA technique is used for feature selection, with the overarching objective of advancing precision and performance metrics in classification tasks. Within the scope of PCOS classification, the proposed framework achieves a commendable 97% accuracy. These results underscore the proficiency of the proposed framework in discriminating PCOS cases with a *
Keywords: ADAYSN, Boruta, cervical cancer, Data balancing, Feature Selection, machine learning, pcos, stacked learning
Received: 10 Jun 2024; Accepted: 30 Jan 2025.
Copyright: © 2025 Emara, El-Shafai, F. Soliman, D. Algarni, Alkanhel and E. Abd El-Samie. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Heba M Emara, University of Menoufia, Shibin Al Kawm, Al Minufiyah, Egypt
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