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ORIGINAL RESEARCH article

Front. Big Data
Sec. Medicine and Public Health
Volume 7 - 2024 | doi: 10.3389/fdata.2024.1435036

A Stacked Learning Framework for Accurate Classification of Polycystic Ovary Syndrome with Advanced Data Balancing and Feature Selection Techniques

Provisionally accepted
Heba M. Emara Heba M. Emara 1*Walid El-Shafai Walid El-Shafai 1Naglaa F. Soliman Naglaa F. Soliman 2Abeer D. Algarni Abeer D. Algarni 2Reem Alkanhel Reem Alkanhel 1,2Fathi E. Abd El-Samie Fathi E. Abd El-Samie 2
  • 1 University of Menoufia, Shibin Al Kawm, Al Minufiyah, Egypt
  • 2 Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia

The final, formatted version of the article will be published soon.

    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: 11 Oct 2024.

    Copyright: © 2024 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

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.