Nam SM, Peterson TA, Seo KY, et al. - This study sought to distinguish essential depression-associated factors using the extreme gradient boosting (XGBoost) machine learning algorithm from big survey data (the Korea National Health and Nutrition Examination Survey, 2012-2016). Researchers intended to achieve a comprehensive understanding of multifactorial features in depression using network analysis. They tested an XGBoost model to classify “current depression” and “no lifetime depression” for a data set of 120 variables for 12,596 cases. Statistical tests on the model and nonmodel factors were conducted using survey-weighted multiple logistic regression and drew a correlation network among factors. This study’s findings demonstrate that XGBoost and network analysis were beneficial to discover depression-related factors and their associations and can be used in epidemiological studies using big survey data.