IYRC Journal
  • Information
    • Editorial Board
    • Reviewer Board
  • Articles
    • 2024
    • 2025 - 1st Issue
  • Guide for authors
  • SUBMISSION
    • Submission system
  • Become a reviewer

Using Machine Learning to Identify Dietary Predictors of Menopausal Symptoms: Insights into Foods Associated with Symptom Severity and Relief

Jeongwon Hur
Blair Academy, New Jersey, USA
​Publication date: May 31, 2025
​DOI: http://doi.org/10.34614/JIYRC2025I11
ABSTRACT 
Menopause is a significant biological transition characterized by hormonal changes that lead to various symptoms, including hot flashes, mood swings, and sleep disturbances. While nutrition has been suggested to influence menopausal symptom severity, limited research has examined the relationship between dietary patterns and symptom relief using advanced analytical methods. This study employs machine learning techniques, specifically Random Forest regression, to analyze publicly available data (N=3,302) from the Study of Women’s Health Across the Nation (SWAN) to identify dietary factors associated with symptom severity and relief. Results suggest that fruit and vegetable consumption is correlated with lower symptom severity, whereas high-fat and high-sodium diets exacerbate symptoms. Long-term supplementation of vitamins C and E was associated with reduced severity of menopausal symptoms, particularly hot flashes and sleep disturbances. These findings underscore the importance of personalized dietary interventions in managing menopause-related health concerns.

PAPER
Download PDF
Picture
THE INTERNATIONAL YOUNG
RESEARCHERS' CONFERENCE


Columbia University Vagelos College of Physicians and Surgeons
104 Haven Ave, New York, NY 10032

一般社団法人 IYRC
   〒106-0032 東京都港区六本木7丁目2番28-605号
    電話番号: 03-3527-9323

    GET IN TOUCH

Submit
  • Information
    • Editorial Board
    • Reviewer Board
  • Articles
    • 2024
    • 2025 - 1st Issue
  • Guide for authors
  • SUBMISSION
    • Submission system
  • Become a reviewer