Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{194344,
        author = {Priyadharshni R and Keerthana G and Rajeshwari T},
        title = {A Preventive Machine Learning Framework for Multi-Level PCOS Risk Evaluation and Lifestyle-Centered Wellness Support},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {3723-3729},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=194344},
        abstract = {Polycystic Ovary Syndrome (PCOS) is a widespread endocrine condition affecting women during their reproductive years. The disorder is influenced not only by biological mechanisms but also by lifestyle behaviors and psychological stress. In many cases, the condition remains unnoticed during its early stages, which may lead to more severe health complications over time. The present study proposes a preventive digital framework that employs machine learning techniques to estimate individual PCOS risk levels and provide lifestyle-based wellness support. The framework integrates multiple categories of user-reported information, including menstrual cycle characteristics, physical symptoms, lifestyle behaviors, and perceived stress indicators. Based on these inputs, the system classifies individuals into different levels of potential risk such as early, moderate, and high risk. In addition, the study examines the relationship between stress patterns and hormonal imbalance that may contribute to PCOS progression. Unlike conventional systems that concentrate mainly on clinical diagnosis after symptoms become evident, the proposed approach emphasizes preventive awareness and behavioral guidance. The framework is designed as a supportive decision-assistance tool rather than a replacement for professional medical diagnosis. Experimental analysis suggests that machine learning-assisted wellness platforms can contribute to early awareness and encourage healthier lifestyle practices for individuals who may be vulnerable to PCOS.},
        keywords = {PCOS, Machine Learning Models, Risk Evaluation, Preventive Wellness, Lifestyle-Centered Healthcare.},
        month = {March},
        }