ORIGINAL RESEARCH article
Front. Public Health
Sec. Public Mental Health
Volume 13 - 2025 | doi: 10.3389/fpubh.2025.1666084
This article is part of the Research TopicAdvances in Artificial Intelligence Applications that Support Psychosocial HealthView all 9 articles
Development of a machine-learning-based predictive model for depression in culturally diverse families: Insights from the Korea Community Health Survey
Provisionally accepted
- 1Gangneung-Wonju National University - Wonju Campus, Wonju-si, Republic of Korea
- 2Sun Moon University, Asan, Republic of Korea
- 3BaekSeok University, Cheonan-si, Republic of Korea
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Background: Although South Korea's overall population is declining, the number of culturally diverse families is increasing. Depression in these families is a significant factor contributing to rising social costs and hindering social integration. Methods: To predict depression in culturally diverse families in South Korea, we analyzed 131 independent variables from 2,568 culturally diverse families who participated in the 2023 Korea Community Health Survey.Results: We identified 15 key predictive variables and evaluated their effects using the XGBoost model, which outperformed 5 other machine learning models. Stress recognition, experience of extreme sadness or despair, subjective health status, age, and frequency of contact with neighbors emerged as significant predictive factors.Conclusions: By conducting a comprehensive analysis of multidimensional indices, this study offers a multifaceted perspective on depression in culturally diverse families.
Keywords: Depression, machine learning, XGBoost, Cultural Diversity, Republic of Korea
Received: 15 Jul 2025; Accepted: 28 Aug 2025.
Copyright: © 2025 Kim, Cha, Jung and Kim. 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: Sunkyung Cha, Sun Moon University, Asan, Republic of Korea
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