Volume 14, Issue 1 (3-2024)
J Health Saf Work 2024, 14(1): 72-91 |
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Kalantary S, Pourhassan B, Beigzadeh Z, Shahbazian V, Jahani A. Investigating the Impact of Health-Protective Behaviors on Morbidity and non-Morbidity COVID-19 among Workers in Oil Refining Industry Using Machine Learning algorithms. J Health Saf Work 2024; 14 (1) :72-91
URL: http://jhsw.tums.ac.ir/article-1-6940-en.html
URL: http://jhsw.tums.ac.ir/article-1-6940-en.html
1- Department of Occupational Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
2- Assessment and Environment Risks Department, Research Center of Environment and Sustainable Development, National Department of Environmet, Tehran, Iran ,ajahani@ut.ac.ir
2- Assessment and Environment Risks Department, Research Center of Environment and Sustainable Development, National Department of Environmet, Tehran, Iran ,
Abstract: (429 Views)
Introduction: The prevalence of COVID-19 has significantly impacted work environments and the workforce. Therefore, identifying the most important preventive and control strategies, as well as assessing their effectiveness, is of paramount importance. Various studies have shown that machine learning algorithms can be used to predict complex and nonlinear issues, including predicting the behavior of various diseases such as COVID-19 and the parameters affecting it, and can be beneficial. The purpose of this study has been to examine the importance of preventive measures and hygiene behaviors in preventing COVID-19 in the oil refining industry using various machine learning models.
Material and Methods: For this purpose, demographic information and health behaviors of individuals were collected. Subsequently, a multi-layer perceptron (MLP), radial basis function (RBF), and support vector machine (SVM) models were compared to enhance the analysis of the effects of preventive measures on COVID-19 infection. Finally, the most influential factors affecting the likelihood of COVID-19 infection were determined using sensitivity analysis.
Results: The results showed that the accuracies achieved in predicting the impact of preventive measures and health behaviors on COVID-19 in occupational settings were 78.1%, 81.2%, and 78.1% by MLP, RBF, and SVM respectively. The RBF model was identified as the most accurate model for predicting the impact of health behaviors on COVID-19 disease Additionally, the level of social distancing with customers, handwashing frequency and disinfection, the availability of cleansing and disinfecting agents for hands and surfaces in the workplace, and gatherings for eating meals and snacks were identified as the most significant health behaviors influencing the prevalence of COVID-19 in the workplace.
Conclusion: Studies of this nature can underscore the importance of attention to preventive measures and health behaviors in unprecedented circumstances. Furthermore, the utilization of artificial intelligence models and tools such as DSS (Decision Support Systems) can serve as powerful tools for optimizing control measures in work environments.
Material and Methods: For this purpose, demographic information and health behaviors of individuals were collected. Subsequently, a multi-layer perceptron (MLP), radial basis function (RBF), and support vector machine (SVM) models were compared to enhance the analysis of the effects of preventive measures on COVID-19 infection. Finally, the most influential factors affecting the likelihood of COVID-19 infection were determined using sensitivity analysis.
Results: The results showed that the accuracies achieved in predicting the impact of preventive measures and health behaviors on COVID-19 in occupational settings were 78.1%, 81.2%, and 78.1% by MLP, RBF, and SVM respectively. The RBF model was identified as the most accurate model for predicting the impact of health behaviors on COVID-19 disease Additionally, the level of social distancing with customers, handwashing frequency and disinfection, the availability of cleansing and disinfecting agents for hands and surfaces in the workplace, and gatherings for eating meals and snacks were identified as the most significant health behaviors influencing the prevalence of COVID-19 in the workplace.
Conclusion: Studies of this nature can underscore the importance of attention to preventive measures and health behaviors in unprecedented circumstances. Furthermore, the utilization of artificial intelligence models and tools such as DSS (Decision Support Systems) can serve as powerful tools for optimizing control measures in work environments.
Keywords: COVID-19, Health Behaviors, Occupational Setting, Morbidity, Machine Learning Algorithm, Sensitivity Analysis
Type of Study: Research |
Received: 2024/03/25 | Accepted: 2024/03/29 | Published: 2024/03/29
Received: 2024/03/25 | Accepted: 2024/03/29 | Published: 2024/03/29
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