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Deep Autoencoder 알고리즘에 기반한 기계설비 기술자의 정신건강 예측 시스템 KCI 등재
Predictive System for Mental Health of Machine Operators Based on Deep Autoencoder Model
한국기계기술학회지
(Journal of the Korean Society of Mechanical Technology)
제26권 제4호 (2024.08)
pp.722-730
pp.722-730
한국기계기술학회
(Korean Society of Mechanical Technology)
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.
키워드
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
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
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