A reliable prediction model of national occupational accident fatality rate can be used to evaluate level of safety and health protection for workers in a country. Moreover, the socio-economic aspects of occupational accidents can be identified through interpretation of a well-organized prediction model. In this paper, we propose a machine learning based relative comparison methods to predict and interpret a national occupational accident fatality rate based on socio-economic indicators. First, we collected 29 years of the relevant data from 11 developed countries. Second, we applied 4 types of machine learning regression models and evaluate their performance. Third, we interpret the contribution of each input variable using Shapley Additive Explanations(SHAP). As a result, Gradient Boosting Regressor showed the best predictive performance. We found that different patterns exist across countries in accordance with different socio-economic variables and occupational accident fatality rate.

Abstract
1. 서 론
2. 문헌 연구
3. 연구 방법
    3.1 분석 방법
    3.2 데이터 수집 및 변수 정의
4. 데이터 분석 및 모델링
    4.1 상관분석 및 종속변수 고찰
    4.2 예측모델 학습
    4.3 SHAP을 활용한 예측모델 해석
5. 결 론
    5.1 산재예측 모델 구현과 활용
    5.2 시사점과 토론 의제
    5.3 연구의 한계 및 향후 연구과제
6. References

• 김경훈(울산대학교 산업안전보건전문학과) | Kyunghun Kim (Department of Occupational Safety and Health, University of Ulsan)
• 이수동(울산대학교 산업안전보건전문학과) | Sudong Lee (Department of Occupational Safety and Health, University of Ulsan) Corresponding Author