ArticleFrontiers in public health2025
Machine learning prediction of depression in culturally diverse families: Findings from the Korea Community Health Survey.
Article in Frontiers in public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
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.
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