ORIGINAL RESEARCH article
Front. Public Health
Sec. Public Mental Health
Volume 13 - 2025 | doi: 10.3389/fpubh.2025.1562280
This article is part of the Research TopicEnvironmental Risk Factors for Depression: Unveiling Pathways to Resilience and Public Mental Health EquityView all 22 articles
Change in Lifestyle and Mental Health in Young Adults: An Exploratory Study with Hybrid Machine Learning
Provisionally accepted
- Kongju National University, Gong, Republic of Korea
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Various mental disorders are becoming increasingly prevalent worldwide. Young adults are particularly vulnerable to mental health issues amid rapid lifestyle changes and socioeconomic pressures. This study adopted hybrid machine learning methods, combining existing statistical analysis and machine learning, to determine which factors affect young adults' mental health, considering recent changes. We used 4-year data (2019-2022) derived from the Community Health Survey, and the final study sample included 141,322 young people aged 19-34. We selected variables based on a literature review and feature selection and performed complex sample logistic regression analysis. New variables that had not previously been discussed (unmet medical needs, chewing difficulty, and accident/addiction experiences) were derived and found to significantly impact depression and subjective stress. These factors' impact on mental health was generally greater than that of the theoretical background variables. In conclusion, this study emphasizes the need to consistently monitor various factors in today's rapidly changing environment when devising policies aimed at managing young adults' mental health.
Keywords: Young Adult, Mental Health, Depression, Subjective stress, machine learning
Received: 17 Jan 2025; Accepted: 22 May 2025.
Copyright: © 2025 Park and Woo. 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: Hyekyung Woo, Kongju National University, Gong, Republic of Korea
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