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논문 국내 국내전문학술지(KCI급) 산재보험 빅데이터를 활용한 장해등급 예측 모델 개발

  • 학술지 구분 국내전문학술지(KCI급)
  • 게재년월 2024-09
  • 저자명 최근호, 김민정, 이정화
  • 학술지명 정보시스템연구
  • 발행처명 한국정보시스템학회
  • 발행국가 국내
  • 논문언어 한국어
  • 전체저자수 3
  • 연구분야 사회과학 > 사회과학일반

논문 초록 (Abstract)

Purpose Prediction model for occupational injuries support more proactive, efficient, and effective policies. This study aims to develop a prediction model for occupational injuries severity, classified into 15 disability grades in South Korea, using machine learning techniques on COMWEL big data. The primary goal is to enhance prediction accuracy, providing a advanced policy tool for early intervention and for evidence-based policy operations. Design/methodology/approach The data analyzed in this study comprises 290,157 administrative records of occupational injuries cases collected from 2018 to 2020 by the Korea Workers' Compensation & Welfare Service, based on the ‘Workers’ Compensation Insurance Application Form’ submitted for occupational injuries treatment. Four machine learning models — Decision Tree, FCNN, XGBoost, and LightGBM — were developed and their performances compared to identify the optimal model. Additionally, the Permutation Feature Importance(PFI) method was employed to estimate the relative contribution of each variable to the performance of the predictive model, thereby identifying key variables. Findings The FCNN algorithm achieved the lowest MAE of 0.7276. Key variables for predicting disability grades included severity index, primary disease code, primary disease site, age at the time of the occupational injuries, and type of industry. This highlights the importance of early policy intervention, and of both medical and socioeconomic factors in model prediction. Academic and policy implications were discussed based on these results.