Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2024 May 23:10:20552076241257046.
doi: 10.1177/20552076241257046. eCollection 2024 Jan-Dec.

Machine-learning model for predicting depression in second-hand smokers in cross-sectional data using the Korea National Health and Nutrition Examination Survey

Na Hyun Kim  1 , Myeongju Kim  2 , Jong Soo Han  1 , Hyoju Sohn  2 , Bumjo Oh  3 , Ji Won Lee  4 , Sumin Ahn  5
Affiliations

Machine-learning model for predicting depression in second-hand smokers in cross-sectional data using the Korea National Health and Nutrition Examination Survey

Na Hyun Kim et al. Digit Health. .

Abstract

Objective: Depression among non-smokers at risk of second-hand smoke (SHS) exposure has been a neglected public health concern despite their vulnerability. The objective of this study was to develop high-performance machine-learning (ML) models for the prediction of depression in non-smokers and to identify important predictors of depression for second-hand smokers.

Methods: ML algorithms were created using demographic and clinical data from the Korea National Health and Nutrition Examination Survey (KNHANES) participants from 2014, 2016, and 2018 (N = 11,463). The Patient Health Questionnaire was used to diagnose depression with a total score of 10 or higher. The final model was selected according to the area under the curve (AUC) or sensitivity. Shapley additive explanations (SHAP) were used to identify influential features.

Results: The light gradient boosting machine (LGBM) with the highest positive predictive value (PPV; 0.646) was selected as the best model among the ML algorithms, whereas the support vector machine (SVM) had the highest AUC (0.900). The most influential factors identified using the LGBM were stress perception, followed by subjective health status and quality of life. Among the smoking-related features, urine cotinine levels were the most important, and no linear relationship existed between the smoking-related features and the values of SHAP.

Conclusions: Compared with the previously developed ML models, our LGBM models achieved excellent and even superior performance in predicting depression among non-smokers at risk of SHS exposure, suggesting potential goals for depression-preventive interventions for non-smokers during public health crises.

Keywords: Depression; machine learning; risk factor; second-hand smoke.

PubMed Disclaimer

Conflict of interest statement

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Figures

Figure 1.
Figure 1.
Data flowchart.
Figure 2.
Figure 2.
ROC curves showing the model's prediction of depression in non-smokers.
Figure 3.
Figure 3.
Results of the SHAP analyses of the LGBM model.
Figure 4.
Figure 4.
Dependence plots of smoking-related features.
See this image and copyright information in PMC

References

    1. American Psychiatric Association. Desk reference to the diagnostic criteria from DSM-5. Washington, DC: American Psychiatric Pub. Incorporated, 2013.
    1. GBD 2019 Mental Disorders Collaborators. Global, regional, and national burden of 12 mental disorders in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet Psych 2022; 9: 137–150. - PMC - PubMed
    1. Harkness KL, Alavi N, Monroe SM, et al. Gender differences in life events before onset of major depressive disorder: the moderating effect of age. J Abnorm Psychol 2010; 119: 791–803. - PMC - PubMed
    1. Müller VI, Cieslik EC, Serbanescu I, et al. Altered brain activity in unipolar depression revisited: meta-analyses of neuroimaging studies. JAMA Psychiatry 2017; 74: 47–55. - PMC - PubMed
    1. Nolen-Hoeksema S. Sex differences in depression. Redwood City: Stanford University Press, 1990.

LinkOut - more resources