Earticle

초록

영어

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.

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

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