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

Abstract

Introduction: In the domain of women's health, the intricate conditions of Polycystic Ovary Syndrome (PCOS) demand sophisticated methodologies for accurate identification and intervention. Methods: This study introduces an innovative machine learning 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. Results: 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 high degree of precision. Critical to this contribution is the rigorous comparative analysis against existing methodologies, affirming the superior accuracy and performance attributes of the proposed framework. Discussion: 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.