Occupational Accidents Prediction in Brazilian States: A Machine Learning Based Approach

J. M. Toledo, J. M. Toledo, Thiago Moura

2024

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.

Paper Citation

in Harvard Style

M. Toledo J. and Moura T. (2024). Occupational Accidents Prediction in Brazilian States: A Machine Learning Based Approach. In Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-989-758-692-7, SciTePress, pages 595-602. DOI: 10.5220/0012557900003690

in Bibtex Style

@conference{iceis24,
author={J. M. Toledo and Thiago Moura},
title={Occupational Accidents Prediction in Brazilian States: A Machine Learning Based Approach},
booktitle={Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2024},
pages={595-602},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012557900003690},
isbn={978-989-758-692-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - Occupational Accidents Prediction in Brazilian States: A Machine Learning Based Approach
SN - 978-989-758-692-7
AU - M. Toledo J.
AU - Moura T.
PY - 2024
SP - 595
EP - 602
DO - 10.5220/0012557900003690
PB - SciTePress