- author = "Sang Min Nam and Thomas A Peterson and Kyoung Yul Seo and Hyun Wook Han and Jee In Kang",
- title = "Discovery of {Depression-Associated} Factors From a Nationwide {Population-Based} Survey: Epidemiological Study Using Machine Learning and Network Analysis",
- journal = "Journal of medical Internet research",
- year = "2021",
- volume = "23",
- number = "6",
- pages = "e27344",
- month = jun # " 24",
- keywords = "genetic algorithms, genetic programming, TPOT, Depression/epidemiology, Epidemiologic Studies, Female, Humans, Machine Learning, Nutrition Surveys, Quality of Life, XGBoost, depression, epidemiology, network, prediction model",
- ISSN = "1439-4456",
-
URL = "
https://www.jmir.org/2021/6/e27344",
-
DOI = "
10.2196/27344",
-
abstract = "BACKGROUND: In epidemiological studies, finding the
best subset of factors is challenging when the number
of explanatory variables is large. OBJECTIVE: Our study
had two aims. First, we aimed to identify 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). Second, we
aimed to achieve a comprehensive understanding of
multifactorial features in depression using network
analysis. METHODS: An XGBoost model was trained and
tested to classify current depression and no lifetime
depression for a data set of 120 variables for 12,596
cases. The optimal XGBoost hyperparameters were set by
an automated machine learning tool (TPOT), and a
high-performance sparse model was obtained by feature
selection using the feature importance value of
XGBoost. We performed statistical tests on the model
and nonmodel factors using survey-weighted multiple
logistic regression and drew a correlation network
among factors. We also adopted statistical tests for
the confounder or interaction effect of selected risk
factors when it was suspected on the network. RESULTS:
The XGBoost-derived depression model consisted of 18
factors with an area under the weighted receiver
operating characteristic curve of 0.86. Two nonmodel
factors could be found using the model factors, and the
factors were classified into direct (P<.05) and
indirect (P ge .05), according to the statistical
significance of the association with depression.
Perceived stress and asthma were the most remarkable
risk factors, and urine specific gravity was a novel
protective factor. The depression-factor network showed
clusters of socioeconomic status and quality of life
factors and suggested that educational level and sex
might be predisposing factors. Indirect factors (eg,
diabetes, hypercholesterolemia, and smoking) were
involved in confounding or interaction effects of
direct factors. Triglyceride level was a confounder of
hypercholesterolemia and diabetes, smoking had a
significant risk in females, and weight gain was
associated with depression involving
diabetes.
CONCLUSIONS: XGBoost and network analysis were useful to discover depression-related factors and their relationships and can be applied to epidemiological studies using big survey data.",
- notes = "PMID: 34184998",
