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.

Abstract
1. 서 론
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
저자 소개

• 권보성(울산대학교 산업경영, 산업안전공학부) | Bosung Kwon (School of Industrial and Safety Engineering, University of Ulsan)
• 엄성원(울산대학교 산업경영, 산업안전공학부) | Sungwon Um (School of Industrial and Safety Engineering, University of Ulsan)
• 정기효(울산대학교 산업경영, 산업안전공학부) | Kihyo Jung (School of Industrial and Safety Engineering, University of Ulsan) Corresponding author