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Machine learning prediction of depression in culturally diverse families: Findings from the Korea Community Health
Geun Myun Kim1, Sunkyung Cha2, Miran Jung3
1Department of Nursing, Gangneung-Wonju National University, Wonju, Gangwon-do, Republic of Korea.
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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