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Predictive System for Mental Health of Machine Operators Based on Deep Autoencoder Model

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초록

This study explores the use of a Deep Autoencoder model to predict depression among plant and machine operators, utilizing data from the Korean National Health and Nutrition Examination Survey (KNHANES, n=3,852). The Deep Autoencoder model outperformed the Logistic Regression, Naive Bayes, XGBoost, and LightGBM models, achieving an accuracy of 86.5%. Key factors influencing depression included work stress, exposure to hazardous substances, and ergonomic conditions. The findings highlight the potential of the Deep Autoencoder model as a robust tool for early identification and intervention in workplace mental health.

Abstract

This study explores the use of a Deep Autoencoder model to predict depression among plant and machine operators, utilizing data from the Korean National Health and Nutrition Examination Survey (KNHANES, n=3,852). The Deep Autoencoder model outperformed the Logistic Regression, Naive Bayes, XGBoost, and LightGBM models, achieving an accuracy of 86.5%. Key factors influencing depression included work stress, exposure to hazardous substances, and ergonomic conditions. The findings highlight the potential of the Deep Autoencoder model as a robust tool for early identification and intervention in workplace mental health.

참고문헌 (24)

  1. American Psychiatric Association, "Diagnostic and statistical manual of mental disorders: DSM-5" (2013), American Psychiatric Association
  2. "Analyzing the last five years (2017-2021) for depression and anxiety disorders" (2023)[URL]
  3. Lee, J., "Polarization and gaps in income and assets:policy implications", Health and Social Welfare Policy Forum, 316, pp.36~50 (2023)
  4. Results of the Cause of Death Statistics in 2021, "KOSTAT", Population Trend Division, Social Statistics Bureau, ~ (2022)
  5. Byun, E, "Factors influencing suicide ideation in college students", Journal of the Korea Academia-Industrial cooperation Society, 21(8), pp.315~324 (2020)
  6. "Depression, why early treatment is important" (2021)[URL]
  7. Go, H., "Depression factors of adult women in a marine city", Journal of Korea Contents Association, 20(1), pp.684~691 (2020)
  8. Park E., "The relationship between adult health risk behavior and subjective health level, stress, and depression", Health Social Welfare Review, 35(1), pp.136~157 (2015)
  9. Han, M., "A Prediction model for psychiatric counseling for depression among subjects with depressive symptoms", Journal of Korean Public Health Nursing, 37(1), pp.125~135 (2023)
  10. Jeong, B., "A Study on the application and comparison of statistical models and machine learning-based techniques for predicting the onset of dementia", Journal of The Korean Data Analysis Society, 22, pp.1819~1834 (2020)
  11. Kim, T., "Analysis of influencing factors of suicide ideation using random forest model: focusing on the national health and nutrition examination survey", Journal of The Korean Data Analysis Society, 25(3), pp.1121~1132 (2023)
  12. Lee, E., "Development of safety monitoring program for psychiatric emergency using google teachable machine", The Journal of the Convergence on Culture Technology, 9(1), pp.613~618 (2023)
  13. Son, D., "Preliminary data of machine-learning approach in prediction of depression in juvenile detention center", Journal of Korean Association of Social Psychiatry, 26(2), pp.86~93 (2021)
  14. Nam, W., "Deep learning based depression classification using environmental factor selection", The Transactions of the Korean Institute of Electrical Engineers, 69(7), pp.1102~1110 (2022)
  15. Kim, K., "Analysis and recognition of depressive emotion through NLP and machine learning", The Journal of the Convergence on Culture Technology, 6(2), pp.449~454 (2020)
  16. Yoo, H., "Depression detection model using multimodal deep learning", Journal of the Korea Institute of Information and Communication Engineering, 27(5), pp.603~610 (2023)
  17. "Korea Health Statistics 2014: Korea national health and nutrition examination survey" (2014)[URL]
  18. Kwag, B., "The Relationship between depression and health-related quality of life in Korean adult: using 2014 Korea national health and nutrition examination survey data", Korean Journal of Family Practice, 8(2), pp.236~243 (2018)
  19. Ratanasiripong, P., "Mental health and burnout among teachers in Thailand", Journal of Humanities and Education Research (2021)
  20. Bartlett, L., "A systematic review and meta-analysis of workplace mindfulness training randomized controlled trials", Journal of Occupational Health Psychology (2019)
  21. Lam, L., "Efficacy of a Workplace Intervention Program With Web-Based Online and Offline Modalities for Improving Workers' Mental Health", Frontiers in Psychiatry, 2022, pp.888157~ (2022)
  22. Almén, N., "Stress-Recovery Management: A Pilot Study Using a Single-Subject Experimental Design", Behavior Modification (2020)
  23. Wang, J., "Effect of Online Psychological Intervention on Burnout in Medical Residents From Different Majors: An Exploratory Study", Frontiers in Psychology (2021)
  24. Liu, S., "Analysis of the Effect of Rational Emotional Intervention Combined with Hierarchical Management Mode on Improving the Psychological Stress of Emergency Nurses and Trainee Nurses", Emergency Medicine International (2022)

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