Earticle

초록

영어

The health and working conditions of employees have become increasingly important issues in modern society. In recent years, there has been a continuous rise in problems related to the deterioration of workers’ alth, which seriously affects their safety and overall quality of life. Although existing research has investigated various factors affecting workers’ health and working conditions, there is still a lack of studies that scientifically analyze and identify key variables from the vast number of factors. This study employs the Lasso (Least Absolute Shrinkage and Selection Operator) technique to mathematically analyze the key variables influencing workers’ health status and satisfaction with their working environment. Lasso is a technique used in machine learning to identify a small number of variables that impact the dependent variable among a large set of variables, thereby reducing model complexity and improving predictive accuracy. The results of the study can be utilized in efficiently improving workers’ health and working environments by focusing on a smaller set of impactful variables.

목차

Abstract
1. 서론
2. 연구 방법
2.1 연구 대상 데이터
2.2 Lasso를 활용한 중요 변수 선정법
2.3 Lasso 분석 시스템 개발
3. 연구 결과
3.1 건강상태
3.2 근로환경 만족도
4. 토의 및 결론
4.1 건강상태
4.2 근로환경 만족도
5. References

키워드

저자

• 권보성 [ Bosung Kwon | 울산대학교 산업경영·산업안전공학부 ]
• 엄성원 [ Sungwon Um | 울산대학교 산업경영·산업안전공학부 ]
• 정기효 [ Kihyo Jung | 울산대학교 산업경영·산업안전공학부 ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  대한안전경영과학회지 [Journal of Korea Safety Management & Science]

• 간기

  계간

• pISSN

  1229-6783

• eISSN

  2288-1484

• 수록기간

  1999~2025

• 십진분류

  KDC 530 DDC 620