Exploring Factors Influencing Depression: Socioeconomic Perspectives Using Machine Learning Analytics

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Depression is a widespread mental health disorder with significant societal impacts, and while socioeconomic status (SES) is a well-established determinant, limited research has explored the unique factors influencing depression in South Korea, such as educational pressure, long working hours, and traditional gender roles. Using data from the Korean National Health and Nutrition Examination Survey (KNHANES) collected in 2014, 2016, 2018, 2020, and 2022, this study analyzed 24,308 participants to examine the relationship between SES and depression. Depression was measured using the Patient Health Questionnaire-9 (PHQ-9), and twelve socioeconomic variables, including income, education, marital status, and working hours, were assessed using logistic regression models. The findings revealed that monthly income, age, marital status, and weekly working hours were significant predictors of depression, with higher income levels unexpectedly associated with greater depression scores, potentially due to increased stress. Gender, household size, and educational attainment were also notable contributors. This study underscores the complex interplay of SES factors and depression in South Korea’s distinct sociocultural context and highlights the need for mental health policies addressing both economic and psychological stressors, particularly for higher income individuals and women. Future research should further explore these dynamics to develop culturally sensitive mental health interventions.