A stacked learning framework for accurate classification of polycystic ovary syndrome with advanced data balancing and feature selection techniques

A stacked learning framework for accurate classification of polycystic ovary syndrome with advanced data balancing and feature selection techniques

IntroductionIn the domain of women’s health, the intricate conditions of Polycystic Ovary Syndrome (PCOS) demand sophisticated methodologies for accurate identification and intervention.MethodsThis study introduces an innovative machine learning framework tailored to precisely classify instances of...

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Bibliographic Details
Main Authors: Heba M. Emara, Walid El-Shafai, Naglaa F. Soliman, Abeer D. Algarni, Reem Alkanhel, Fathi E. Abd El-Samie
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-05-01
Series:Frontiers in Physiology
Subjects:
ADAYSN
boruta
cervical cancer
data balancing
feature selection
machine learning
Online Access:https://www.frontiersin.org/articles/10.3389/fphys.2025.1435036/full
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https://www.frontiersin.org/articles/10.3389/fphys.2025.1435036/full

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