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Integrated feature selection and classification algorithm in the prediction of work-related accidents in the retail sector: a comparative study

2022Conference paper Open access
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dc.contributor.authorSena, Inês
dc.contributor.authorLima, Laíres
dc.contributor.authorSilva, Felipe G.
dc.contributor.authorBraga, Ana Cristina
dc.contributor.authorNovais, Paulo
dc.contributor.authorFernandes, Florbela P.
dc.contributor.authorPacheco, Maria F.
dc.contributor.authorVaz, Clara
dc.contributor.authorLima, José
dc.contributor.authorPereira, Ana I.
dc.date.accessioned2023-02-28T11:08:37Z
dc.date.available2023-02-28T11:08:37Z
dc.date.issued2022
dc.description.abstractAssessing the different factors that contribute to accidents in the workplace is essential to ensure the safety and well-being of employees. Given the importance of risk identification in hazard prediction, this work proposes a comparative study between different feature selection techniques (χ2 test and Forward Feature Selection) combined with learning algorithms (Support Vector Machine, Random Forest, and Naive Bayes), both applied to a database of a leading company in the retail sector, in Portugal. The goal is to conclude which factors of each database have the most significant impact on the occurrence of accidents. Initial databases include accident records, ergonomic workplace analysis, hazard intervention and risk assessment, climate databases, and holiday records. Each method was evaluated based on its accuracy in the forecast of the occurrence of the accident. The results showed that the Forward Feature Selection-Random Forest pair performed better among the assessed combinations, considering the case study database. In addition, data from accident records and ergonomic workplace analysis have the largest number of features with the most significant predictive impact on accident prediction. Future studies will be carried out to evaluate factors from other databases that may have meaningful information for predicting accidents.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) 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; Lima, Laíres; Silva, Felipe G.; Braga, Ana Cristina; Novais, Paulo; Fernandes, Florbela P.; Pacheco, Maria F.; Vaz, Clara B.; Lima, José; Pereira, Ana I. (2022). Integrated feature selection and classification algorithm in the prediction of work-related accidents in the retail sector: a comparative study. In 2nd International Conference on Optimization, Learning Algorithms and Applications, OL2A 2022. Bragançapt_PT
dc.identifier.urihttp://hdl.handle.net/10198/27281
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.relationIntelligent system for occupational safety in retail sector
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectFeature selectionpt_PT
dc.subjectClassification algorithmspt_PT
dc.subjectAccident predictionpt_PT
dc.titleIntegrated feature selection and classification algorithm in the prediction of work-related accidents in the retail sector: a comparative studypt_PT
dc.typeconference paper
dspace.entity.typePublication
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardTitleIntelligent system for occupational safety in retail sector
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F05757%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F05757%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/OE/UI%2FBD%2F153348%2F2022/PT
oaire.citation.conferencePlaceBragançapt_PT
oaire.citation.title2nd International Conference on Optimization, Learning Algorithms and Applications, OL2A 2022pt_PT
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
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person.identifier.ridF-1519-2016
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project.funder.identifierhttp://doi.org/10.13039/501100001871
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project.funder.nameFundação para a Ciência e a Tecnologia
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rcaap.rightsopenAccesspt_PT
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