Semi-supervised MarginBoost를 이용한 기능, 설비, 기계분야 근로자의 업무상 손상 예측 시스템 Identifying Determinants of Occupational Injuries Among Plant and Machine Operators Using Semi-supervised MarginBoost
한국기계기술학회지
(韓國機械技術學會誌)
제26권 제4호
(2024.08)
pp.627-634
pp.627-634
한국기계기술학회
(Korean Society of Mechanical Technology)
초록
This study examines factors influencing occupational injuries among plant and machine operators using the Semi-supervised MarginBoost algorithm. Data from the 2007-2009 Korean National Health and Nutrition Examination Survey (KNHANES) were analyzed, covering 4,062 employed participants. The MarginBoost model achieved 84.3% accuracy, outperforming other models. Key factors identified included exposure to hazardous substances, ergonomic conditions, and psychosocial stress. The findings emphasize the need for targeted interventions to enhance workplace safety and offer a robust predictive tool for the effective management of occupational health.
키워드
목차
Abstract
1. Introduction
2. Materials and Methods
2.1. Study Design and Population
2.2. Data Collection
2.3. Input Variables
2.4. Data Preprocessing
2.5. Machine Learning Models
2.6. MarginBoost Algorithm
2.7. Model Training and Evaluation
2.8. Feature Importance
3. Results
3.1 Health-Related Characteristics
3.2. Work Environment Characteristics
3.3. Occupational Injury Rates
3.4. Model Performance
3.5. Feature Importance
4. Discussion
References
1. Introduction
2. Materials and Methods
2.1. Study Design and Population
2.2. Data Collection
2.3. Input Variables
2.4. Data Preprocessing
2.5. Machine Learning Models
2.6. MarginBoost Algorithm
2.7. Model Training and Evaluation
2.8. Feature Importance
3. Results
3.1 Health-Related Characteristics
3.2. Work Environment Characteristics
3.3. Occupational Injury Rates
3.4. Model Performance
3.5. Feature Importance
4. Discussion
References
저자