Use of Artificial Neural Networks (ANNs) for the Analysis and Modeling of Factors That Affect Occupational Injuries in Large Construction Industries.

Iraj Mohammadfam, Ahmad Soltanzadeh, Abbas Moghimbeigi, Behrouz Alizadeh Savareh

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Article in Electronic physician, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

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5citing papers in PubMed, 1 pooled it

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3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 1 synthesis or guideline pooled it.

Global prevalence of occupational injuries among sanitation workers: a systematic review and meta-analysis.Frontiers in public health · 2024
Pooled it
Analysis of the severity of occupational injuries in the mining industry using a Bayesian network.Epidemiology and health · 2019
Article
Quantitative EEG features selection in the classification of attention and response control in the children and adolescents with attention deficit hyperactivity disorder.Future science OA · 2018
Article
Performance comparison of machine learning techniques in sleep scoring based on wavelet features and neighboring component analysis.PeerJ · 2018
Article
Modeling of Individual and Organizational Factors Affecting Traumatic Occupational Injuries Based on the Structural Equation Modeling: A Case Study in Large Construction Industries.Archives of trauma research · 2016
Article

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5 · Who and what money

Authors and funding

4 authors.

Iraj MohammadfamPh.D. of Occupational Hygiene Engineering, Associate Professor, Department of Occupational Hygiene Engineering, School of Public Health and Research Center for Health Sciences, Hamadan University of Medical Sciences, Hamadan, Iran.

Ahmad SoltanzadehPh.D. Candidate of Occupational Hygiene Engineering, Department of Occupational Hygiene Engineering, School of Public Health and Research Center for Health Sciences, Hamadan University of Medical Sciences, Hamadan, Iran.

Abbas MoghimbeigiPh.D. of Biostatistics, Associate Professor, Department of Biostatistics and Epidemiology and Modeling of Non-communicable Disease Research Canter, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran.

Behrouz Alizadeh SavarehPh.D. Candidate of Medical Informatics, Department of Medical Informatics, Allied Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionOccupational injuries as a workforce's health problem are very important in large-scale workplaces. Analysis and modeling the health-threatening factors are good ways to promote the workforce's health and a fundamental step in developing health programs. The purpose of this study was ANN modeling of the severity of occupational injuries to determine the health-threatening factors and to introduce a model to predict the severity of occupational injuries.

methodsThis analytical chain study was conducted in 10 large construction industries during a 10-year period (2005-2014). Nine hundred sixty occupational injuries were analyzed and modeled based on feature weighting by the rough set theory and artificial neural networks (ANNs). Two analytical software programs, i.e., RSES and MATLAB 2014 were used in the study.

resultsThe severity of occupational injuries was calculated as 557.47 ± 397.87 days. The findings of both models showed that the injuries' severity as a health problem resulted in various factors, including individual, organizational, health and safety (H&S) training, and risk management factors, which could be considered as causal and predictive factors of accident severity rate (ASR).

conclusionThe results indicated that ANNs were a reliable tool that can be used to analyze and model the severity of occupational injuries as one of the important health problems in large-scale workplaces. Additionally, the combination of rough set and ANNs is a good and proper chain approach to modeling the factors that threaten the health of workforces and other H&S problems.

Indexed as

accident severity rate (ASR)artificial neural networks (ANN)occupational injuryrough set theoryworkforce’s health

Identifiers

PMID26767107

DOI10.19082/1515

PMCPMC4700899

What OpenQuestion holds

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LicenceCC BY-NC-ND

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.