Exploring Features to Classify Occupational Accidents in the Retail Sector

Sena, Inês; Braga, Ana Cristina; Novais, Paulo; Fernandes, Florbela P.; Pacheco, Maria F.; Vaz, Clara B.; Lima, José; Pereira, Ana I.

http://hdl.handle.net/10198/30340

Use this identifier to reference this record.

Name:	Description:	Size:	Format:
Exploring Features to Classify Occupational Accidents.pdf		490.38 KB	Adobe PDF	Download

Send Feedback

Authors

Sena, Inês

Braga, Ana Cristina

Novais, Paulo

Fernandes, Florbela P.

Abstract(s)

The Machine Learning approach is used in several application domains, and its exploitation in predicting accidents in occupational safety is relatively recent. The present study aims to apply different Machine Learning algorithms for classifying the occurrence or nonoccurrence of accidents at work in the retail sector. The approach consists of obtaining an impact score for each store and work unit, considering two databases of a retail company, the preventive safety actions, and the action plans. Subsequently, each score is associated with the occurrence or non-occurrence of accidents during January and May 2023. Of the five classification algorithms applied, the Support Vector Machine was the one that obtained the best accuracy and precision values for the preventive safety actions. As for the set of actions plan, the Logistic Regression reached the best results in all calculated metrics. With this study, estimating the impact score of the study variables makes it possible to identify the occurrence of accidents at work in the retail sector with high precision and accuracy.

Keywords

Workplace Accidents Classification Machine Learning algorithms Score Impact

URI

http://hdl.handle.net/10198/30340

Citation

Sena, Inês; Braga, Ana Cristina; Novais, Paulo; Fernandes, Florbela P.; Pacheco, Maria F.; Vaz, Clara B.; Lima, José; Pereira, Ana I. (2024). Exploring Features to Classify Occupational Accidents in the Retail Sector. In 3rd International Conference on Optimization, Learning Algorithms and Applications (OL2A 2023). Cham: Springer Nature, Vol. 1, p. 49–62. ISBN 978-3-031-53024-1.