머신러닝을 활용한 사회ㆍ경제지표 기반 산재 사고사망률 상대비교 방법론
Socioeconomic Indicators Based Relative Comparison Methodology of National Occupational Accident Fatality Rates Using Machine Learning
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.
목차
Abstract 1. 서론 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. 결론 5.1 산재예측 모델 구현과 활용 5.2 시사점과 토론 의제 5.3 연구의 한계 및 향후 연구과제 6. References