Data mining in occupational safety and health: a systematic mapping and roadmap

Reis, Beatriz Lavezo dos; Rosa, Ana Caroline Francisco da; Machado, Ageu de Araújo; Wencel, Simone Luzia Santana Sambugaro; Leal, Gislaine Camila Lapasini; Galdámez, Edwin Vladimir Cardoza; Souza, Rodrigo Clemente Thom de

doi:10.1590/0103-6513.20210048

Cited by 3 publications

(3 citation statements)

References 76 publications

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“…During 2020, the NSSI registered about 446.9 thousand work accidents in Brazil involving employees in standard employment relationships, a decrease of approximately 30% compared to 2019 [33]. Although statistics on fatal accidents at work keep being produced, studies evaluating workers' deaths in Brazil are scarce, especially considering that most data refer only to insured workers [27].…”

Section: Work Accidentsmentioning

confidence: 99%

“…The National Institute of Social Security (NISS), responsible for ensuring victims' rights, annually makes the Work Accident Communication (WAC) data available to the public, since 2008 [2]. Given the volume of data produced on accidents, occupational diseases and deaths, data mining (DM) is an alternative to develop preventive programs and policies [15,27]. The use of data mining and machine learning facilitate the transformation of a large volume of data into useful information, as well as the detection of patterns and trends, making it possible to predict and discover parameters for occurrences through analysis [28,38].…”

Section: Introductionmentioning

confidence: 99%

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Identifying relevant factors about work accidents in the road transport sector and the deaths relation in this scenario

Silva

Reis²,

Jesus³

et al. 2023

DYNA

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Workers’ health and safety are a major concern in society, since work accidents have a major impact on productivity and economy. In Brazil, the accidents are officially reported through Work Accident Communication and they are available to the public. Thus, this study analyzed a balanced dataset containing 1,206 records of deaths caused by work accidents related to the transport sector. Its aim was analyzing how the deaths in the transport sector are related with the other work accident factors. To achieve this goal, twelve performance data mining techniques are compared, through five performance metrics, regarding the predictive capacity of the occurrence of deaths caused by work accidents. In this context, the XGBoost and Naïve Bayes algorithms showed the best predictive capacity. The explanatory analysis indicates that work accidents followed by death in road transport are predictable due to the severity of the injuries and vital parts of the body are affected.

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Section: Work Accidentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Identifying relevant factors about work accidents in the road transport sector and the deaths relation in this scenario

Silva

Reis²,

Jesus³

et al. 2023

DYNA

Self Cite

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“…Data mining methods are applicable in various fields, dealing with different types of data and objectives. Studies focusing on DM techniques applied to construction safety date back to no later than 2014 [52]. The study has been developed as part of the 2022-2024 Inail's research program and the objective "Study of the effectiveness and efficiency indices related to innovative technologies aimed at preventing the risk of injury in highly variable work environments".…”

Section: Introductionmentioning

confidence: 99%

A Review of Data Mining Strategies by Data Type, with a Focus on Construction Processes and Health and Safety Management

Pireddu,

Bedini,

Lombardi

et al. 2024

Preprint

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Increasingly, information technology facilitates the storage and management of data useful for risk analysis and event prediction. Studies on data extraction related to occupational health and safety are increasingly available; however, due to its variability, the construction sector warrants special attention. This review is conducted under the research programmes of the National Institute for Occupational Accident Insurance (Inail). Objectives: The research question focuses on identifying which data mining (DM) methods, among supervised, unsupervised, and others, are most appropriate to be applied to certain investigation objectives, types, and sources of data, as defined by the authors. Methods: Scopus and ProQuest were the main sources from which we extracted studies in the field of construction, published between 2014 and 2023. The eligibility criteria applied in the selection of studies, were based on the Preferred Reporting Items for Systematic Review and meta-analysis (PRISMA). For exploratory purposes, we applied hierarchical clustering, while for in-depth analysis, we use principal component analysis (PCA) and meta-analysis. Results: The search strategy based on the PRISMA eligibility criteria, provided us with 61 out of 2,234 potential articles, 202 observation, 91 methodologies, 4 survey purposes, 3 data sources, 7 data types, and 3 resource type. Cluster analysis and PCA organized the information included in the paper dataset into two dimensions and labels: "supervised methods, institutional dataset, and predictive and classificatory purposes" (correlation 0.97÷8.18E-01; p-value 7.67E-55÷1.28E-22) and the second, Dim2 "not-supervised methods; project, simulation, literature, text data; monitoring, decision-making processes; machinery and environment" (corr. 0.84÷0.47; p-value 5.79E-25÷3.59E-06). We answered the research question regarding which method, among supervised, unsupervised, or other, is most suitable for application to data in the construction industry. Conclusions: The meta-analysis provided an overall estimate of the better effectiveness of supervised methods (Odds Ratio = 0.71, Confidence Interval 0.53÷0.96) compared to not-supervised methods.

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A Review of Data Mining Strategies by Data Type, with a Focus on Construction Processes and Health and Safety Management

Pireddu,

Bedini,

Lombardi

et al. 2024

IJERPH

View full text Add to dashboard Cite

Increasingly, information technology facilitates the storage and management of data useful for risk analysis and event prediction. Studies on data extraction related to occupational health and safety are increasingly available; however, due to its variability, the construction sector warrants special attention. This review is conducted under the research programs of the National Institute for Occupational Accident Insurance (Inail). Objectives: The research question focuses on identifying which data mining (DM) methods, among supervised, unsupervised, and others, are most appropriate for certain investigation objectives, types, and sources of data, as defined by the authors. Methods: Scopus and ProQuest were the main sources from which we extracted studies in the field of construction, published between 2014 and 2023. The eligibility criteria applied in the selection of studies were based on the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA). For exploratory purposes, we applied hierarchical clustering, while for in-depth analysis, we used principal component analysis (PCA) and meta-analysis. Results: The search strategy based on the PRISMA eligibility criteria provided us with 63 out of 2234 potential articles, 206 observations, 89 methodologies, 4 survey purposes, 3 data sources, 7 data types, and 3 resource types. Cluster analysis and PCA organized the information included in the paper dataset into two dimensions and labels: “supervised methods, institutional dataset, and predictive and classificatory purposes” (correlation 0.97–8.18 × 10−1; p-value 7.67 × 10−55–1.28 × 10−22) and the second, Dim2 “not-supervised methods; project, simulation, literature, text data; monitoring, decision-making processes; machinery and environment” (corr. 0.84–0.47; p-value 5.79 × 10−25–-3.59 × 10−6). We answered the research question regarding which method, among supervised, unsupervised, or other, is most suitable for application to data in the construction industry. Conclusions: The meta-analysis provided an overall estimate of the better effectiveness of supervised methods (Odds Ratio = 0.71, Confidence Interval 0.53–0.96) compared to not-supervised methods.

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Data mining in occupational safety and health: a systematic mapping and roadmap

Cited by 3 publications

References 76 publications

Identifying relevant factors about work accidents in the road transport sector and the deaths relation in this scenario

Identifying relevant factors about work accidents in the road transport sector and the deaths relation in this scenario

A Review of Data Mining Strategies by Data Type, with a Focus on Construction Processes and Health and Safety Management

A Review of Data Mining Strategies by Data Type, with a Focus on Construction Processes and Health and Safety Management

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