An integration of intelligent approaches and economic criteria for predictive analytics of occupational accidents
Autor(a) principal: | |
---|---|
Data de Publicação: | 2023 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.22/25127 |
Resumo: | Occupational accidents are a significant concern, resulting in human suffering, economic crises, and social issues. Despite ongoing efforts to comprehend their causes and predict their occurrences, the use of machine learning models in this domain remains limited. This study aims to address this gap by investigating intelligent approaches that incorporate economic criteria to predict occupational accidents. Four machine learning algorithms, Random Forest (RF), Support Vector Machine (SVM), Multivariate Adaptive Regression Spline (MARS), and M5 Tree Model (M5), were employed to predict occupational accidents, considering three economic criteria: basic income (BI), inflation index (II), and price index (PI). The study focuses on identifying the most suitable model for predicting the frequency of occupational accidents (FOA) and determining the economic criteria with the greatest influence. The results reveal that the RF model accurately predicts accidents across all income levels. Additionally, among the economic criteria, II had the most significant impact on accidents. The findings suggest that a reduction in FOA is unlikely in the coming years due to the increasing growth of II and PI, coupled with a slight annual increase in BI. Implementing appropriate countermeasures to enhance workers’ economic welfare, particularly for low-income employees, is crucial for reducing occupational accidents. This research underscores the potential of machine learning models in predicting and preventing occupational accidents while highlighting the critical role of economic factors. It contributes valuable insights for scholars, practitioners, and policymakers to develop effective strategies and interventions to improve workplace safety and workers’ economic well-being |
id |
RCAP_b10536465f25482d77a7a1f7249fc7b8 |
---|---|
oai_identifier_str |
oai:recipp.ipp.pt:10400.22/25127 |
network_acronym_str |
RCAP |
network_name_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository_id_str |
7160 |
spelling |
An integration of intelligent approaches and economic criteria for predictive analytics of occupational accidentsOccupational accidentsPredictive analysisPredictive modelingEconomic criteriaMachine learningWorkplace safetyOccupational accidents are a significant concern, resulting in human suffering, economic crises, and social issues. Despite ongoing efforts to comprehend their causes and predict their occurrences, the use of machine learning models in this domain remains limited. This study aims to address this gap by investigating intelligent approaches that incorporate economic criteria to predict occupational accidents. Four machine learning algorithms, Random Forest (RF), Support Vector Machine (SVM), Multivariate Adaptive Regression Spline (MARS), and M5 Tree Model (M5), were employed to predict occupational accidents, considering three economic criteria: basic income (BI), inflation index (II), and price index (PI). The study focuses on identifying the most suitable model for predicting the frequency of occupational accidents (FOA) and determining the economic criteria with the greatest influence. The results reveal that the RF model accurately predicts accidents across all income levels. Additionally, among the economic criteria, II had the most significant impact on accidents. The findings suggest that a reduction in FOA is unlikely in the coming years due to the increasing growth of II and PI, coupled with a slight annual increase in BI. Implementing appropriate countermeasures to enhance workers’ economic welfare, particularly for low-income employees, is crucial for reducing occupational accidents. This research underscores the potential of machine learning models in predicting and preventing occupational accidents while highlighting the critical role of economic factors. It contributes valuable insights for scholars, practitioners, and policymakers to develop effective strategies and interventions to improve workplace safety and workers’ economic well-beingElsevierRepositório Científico do Instituto Politécnico do PortoGholamizadeh, KamranZarei, EsmaeilYazdi, MohammadRodrigues, Matilde A.Shirmohammadi-Khorram, NasrinMohammadfam, Iraj2024-03-05T14:18:40Z2023-122023-12-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.22/25127engGholamizadeh, K., Zarei, E., Yazdi, M., Rodrigues, M. A., shirmohammadi-Khorram, N., & Mohammadfam, I. (2023). An integration of intelligent approaches and economic criteria for predictive analytics of occupational accidents. Decision Analytics Journal, 9, 100357. https://doi.org/10.1016/j.dajour.2023.10035710.1016/j.dajour.2023.100357info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-13T01:48:32Zoai:recipp.ipp.pt:10400.22/25127Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:13:27.685789Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
An integration of intelligent approaches and economic criteria for predictive analytics of occupational accidents |
title |
An integration of intelligent approaches and economic criteria for predictive analytics of occupational accidents |
spellingShingle |
An integration of intelligent approaches and economic criteria for predictive analytics of occupational accidents Gholamizadeh, Kamran Occupational accidents Predictive analysis Predictive modeling Economic criteria Machine learning Workplace safety |
title_short |
An integration of intelligent approaches and economic criteria for predictive analytics of occupational accidents |
title_full |
An integration of intelligent approaches and economic criteria for predictive analytics of occupational accidents |
title_fullStr |
An integration of intelligent approaches and economic criteria for predictive analytics of occupational accidents |
title_full_unstemmed |
An integration of intelligent approaches and economic criteria for predictive analytics of occupational accidents |
title_sort |
An integration of intelligent approaches and economic criteria for predictive analytics of occupational accidents |
author |
Gholamizadeh, Kamran |
author_facet |
Gholamizadeh, Kamran Zarei, Esmaeil Yazdi, Mohammad Rodrigues, Matilde A. Shirmohammadi-Khorram, Nasrin Mohammadfam, Iraj |
author_role |
author |
author2 |
Zarei, Esmaeil Yazdi, Mohammad Rodrigues, Matilde A. Shirmohammadi-Khorram, Nasrin Mohammadfam, Iraj |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
Repositório Científico do Instituto Politécnico do Porto |
dc.contributor.author.fl_str_mv |
Gholamizadeh, Kamran Zarei, Esmaeil Yazdi, Mohammad Rodrigues, Matilde A. Shirmohammadi-Khorram, Nasrin Mohammadfam, Iraj |
dc.subject.por.fl_str_mv |
Occupational accidents Predictive analysis Predictive modeling Economic criteria Machine learning Workplace safety |
topic |
Occupational accidents Predictive analysis Predictive modeling Economic criteria Machine learning Workplace safety |
description |
Occupational accidents are a significant concern, resulting in human suffering, economic crises, and social issues. Despite ongoing efforts to comprehend their causes and predict their occurrences, the use of machine learning models in this domain remains limited. This study aims to address this gap by investigating intelligent approaches that incorporate economic criteria to predict occupational accidents. Four machine learning algorithms, Random Forest (RF), Support Vector Machine (SVM), Multivariate Adaptive Regression Spline (MARS), and M5 Tree Model (M5), were employed to predict occupational accidents, considering three economic criteria: basic income (BI), inflation index (II), and price index (PI). The study focuses on identifying the most suitable model for predicting the frequency of occupational accidents (FOA) and determining the economic criteria with the greatest influence. The results reveal that the RF model accurately predicts accidents across all income levels. Additionally, among the economic criteria, II had the most significant impact on accidents. The findings suggest that a reduction in FOA is unlikely in the coming years due to the increasing growth of II and PI, coupled with a slight annual increase in BI. Implementing appropriate countermeasures to enhance workers’ economic welfare, particularly for low-income employees, is crucial for reducing occupational accidents. This research underscores the potential of machine learning models in predicting and preventing occupational accidents while highlighting the critical role of economic factors. It contributes valuable insights for scholars, practitioners, and policymakers to develop effective strategies and interventions to improve workplace safety and workers’ economic well-being |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-12 2023-12-01T00:00:00Z 2024-03-05T14:18:40Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.22/25127 |
url |
http://hdl.handle.net/10400.22/25127 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Gholamizadeh, K., Zarei, E., Yazdi, M., Rodrigues, M. A., shirmohammadi-Khorram, N., & Mohammadfam, I. (2023). An integration of intelligent approaches and economic criteria for predictive analytics of occupational accidents. Decision Analytics Journal, 9, 100357. https://doi.org/10.1016/j.dajour.2023.100357 10.1016/j.dajour.2023.100357 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Elsevier |
publisher.none.fl_str_mv |
Elsevier |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
|
_version_ |
1799137788038217728 |