Authors:
J. M. Toledo
1
;
2
and
Thiago Moura
1
Affiliations:
1
Federal Institute of Paraíba (IFPB), Avenida Primeiro de Maio, 720, Jaguaribe, João Pessoa, Paraíba, CEP 58015-435, Brazil
;
2
Ministério do Trabalho e Emprego, Esplanada dos Ministérios, Bloco F, Brasília, DF, Brazil
Keyword(s):
Occupational Accidents, Machine Learning, Regression Problems.
Abstract:
Occupational accident is an unexpected event connected to work that may result in injury and/or death of workers. Thus, the possibility of predicting the occurrence of occupational accidents can assist the government in labor policy-making, protecting the lives and health of workers. In this work, we propose the use of machine learning models to predict the occurrence of occupational accidents in each Brazillian state. We use multiple datasets concerning socio-economic, employment, and demographic data as sources to obtain an integrated table utilized to train regression models (linear regression, support vector regressor, and LightGBM) and make predictions. We verify that the developed models show high predictive performance and explainability, with the R-squared metric reaching 0.90.