Change in lifestyle and mental health in young adults: an exploratory study with hybrid machine learning - Park, Nayoung; Woo, Hyekyung (Frontiers in Public Health, 2025)
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.