Earticle

초록

영어

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.

목차

Abstract
1. Introduction
2. Materials and Methods
2.1. Design and Subject
2.2. Data Collection
2.3. Input Variables
2.4. Data Preprocessing
2.5. Machine Learning Models
2.6. Deep Autoencoder Model
2.7. Model Training and Evaluation
2.8. Feature Importance
3. Results
3.1. Descriptive Statistics
3.2. Model Performance
3.3. Model Performance Comparison
3.4. Feature Importance
3.5. Feature Importance Analysis
4. Discussion
5. Discussion
References

키워드

저자

• 변해원 [ Haewon Byeon | Department of AI-Software, Inje University, South Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국기계항공기술학회지(구 한국기계기술학회지) [Journal of the Korean Society of Mechanical and Aviation Technology ]

• 간기

  격월간

• pISSN

  1229-604X

• eISSN

  2508-3805

• 수록기간

  1999~2026

• 등재여부

  KCI 등재

• 십진분류

  KDC 550 DDC 620