earticle

영어

This study aims to select the factors affecting depression in young people and to identify the relationship by using a hybrid machine learning. From 2018 to 2022, a total of four years of community health survey data were used, and the final subjects of the study were 141,510 young people aged 19 to 34. In order to identify the factors that significantly affect depression, a feature selection with Least absolute shrinkage and selection operator (LASSO) regression has been applied. After selecting variables, logistic regression analysis was performed through a complex sampling design. Through feature selection, variables such as health behavior (smoking, drinking, sleeping) and health condition (with or without hypertension) were selected. Among the variables related to health behavior, smoking (OR 1.95; 95% CI 1.82 - 2.09) and sleep_7 hours or more (OR 0.70; 95% CI 0.66 - 0.74) had a significant effect on depression. Both the hypertension and subjective health level, which are variables related to health status, had a significant effect on depression.