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Exploring Features to Classify Occupational Accidents in the Retail Sector

2024Conference paper Open access
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dc.contributor.authorSena, Inês
dc.contributor.authorBraga, Ana Cristina
dc.contributor.authorNovais, Paulo
dc.contributor.authorFernandes, Florbela P.
dc.contributor.authorPacheco, Maria F.
dc.contributor.authorVaz, Clara B.
dc.contributor.authorLima, José
dc.contributor.authorPereira, Ana I.
dc.date.accessioned2024-10-08T08:28:58Z
dc.date.available2024-10-08T08:28:58Z
dc.date.issued2024
dc.description.abstractThe 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.pt_PT
dc.description.sponsorshipThe authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/MCTES (PIDDAC) to CeDRI (UIDB/05757/2020 and UIDP/05757/2020), ALGORITMI UIDB/00319/2020 and SusTEC (LA/P/0007/2021). This work has been supported by NORTE-01-0247-FEDER-072598 iSafety: Intelligent system for occupational safety and well-being in the retail sector. Inˆes Sena was supported by FCT PhD grant UI/BD/153348/2022.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationSena, 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.pt_PT
dc.identifier.doi10.1007/978-3-031-53025-8_4pt_PT
dc.identifier.isbn978-3-031-53025-8
dc.identifier.isbn978-3-031-53024-1
dc.identifier.urihttp://hdl.handle.net/10198/30340
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherSpringer Naturept_PT
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.relationALGORITMI Research Center
dc.relationAssociate Laboratory for Sustainability and Tecnology in Mountain Regions
dc.relationIntelligent system for occupational safety in retail sector
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectWorkplace Accidents Classificationpt_PT
dc.subjectMachine Learning algorithmspt_PT
dc.subjectScore Impactpt_PT
dc.titleExploring Features to Classify Occupational Accidents in the Retail Sectorpt_PT
dc.typeconference paper
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oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
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oaire.awardTitleALGORITMI Research Center
oaire.awardTitleAssociate Laboratory for Sustainability and Tecnology in Mountain Regions
oaire.awardTitleIntelligent system for occupational safety in retail sector
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oaire.citation.endPage62pt_PT
oaire.citation.startPage49pt_PT
oaire.citation.title3rd International Conference on Optimization, Learning Algorithms and Applications (OL2A 2023)pt_PT
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