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Volume 11, Issue 4 (Iranian Journal of Ergonomics- In Press - 2024)
Iran J Ergon 2024, 11(4): 0-0 |
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Nazari M, Sammak Amani A, Mououdi M A, Alyan nezhadi M M. Prediction of musculoskeletal disorders based on people's demographic information using artificial intelligence methods and CMDQ questionnaire. Iran J Ergon 2024; 11 (4)
URL: http://journal.iehfs.ir/article-1-993-en.html
URL: http://journal.iehfs.ir/article-1-993-en.html
1- Assistant Professor, Department of Computer Science, University of Science and Technology of Mazandaran, Behshahr, Iran
2- MSc Occupational Health Engineering, Department of Occupational Health, Mazandaran University of Medical Sciences, Sari, Iran. , arezoo.sam76@yahoo.com
3- Faculty of Health, Department of Occupational Health, Mazandaran University of Medical Sciences, Sari, Iran
2- MSc Occupational Health Engineering, Department of Occupational Health, Mazandaran University of Medical Sciences, Sari, Iran. , arezoo.sam76@yahoo.com
3- Faculty of Health, Department of Occupational Health, Mazandaran University of Medical Sciences, Sari, Iran
Abstract: (463 Views)
Introduction: Work-Related Musculoskeletal Disorders (WMSDs) are the most significant challenges in both developing and developed countries, affecting the majority of individuals throughout their lives. Considering the detrimental effects of musculoskeletal disorders on the productivity and general health of employees, this research utilizes the Cornell Musculoskeletal Disorder Questionnaire (CMDQ) to develop an intelligent model for assessing and predicting the levels of musculoskeletal disorders.
Methods:In this descriptive-analytical study, 810 employees from five organizations (in four occupational categories including administrative, technical, production, and services) completed the CMDQ, voluntarily. After collecting questionnaire data and performing relevant statistical analyses, data normalization and clustering based on the K-Means method were used to determine levels of musculoskeletal disorders. Finally, the multilayer perceptron artificial neural network was trained to predict the levels of musculoskeletal disorders and the criteria of precision, accuracy, recall and F1-score were used to evaluate the proposed model.
Results: The performance of the proposed model in predicting the levels of musculoskeletal disorders are presented in two scenarios (using and not using the SMOTE method) based on the evaluation criteria provided.
The accuracy, precision, Recall and F1-score values were 0.724, 0.709, 0.756 and 720 respectively. The appropriate accuracy and precision in the proposed model indicate its capability in identifying the levels of musculoskeletal disorders in individuals and help healthcare professionals take the necessary measures to prevent and predict them.
Conclusion: This study employs the CMDQ questionnaire and artificial intelligence methods to analyze musculoskeletal disorders in the workplace. The proposed model demonstrates significant accuracy and precision compared to similar studies. The results indicate that this model can be utilized for the identification and prediction of musculoskeletal disorders in organizational employees, offering the potential to expedite the identification process and reduce costs.
Methods:In this descriptive-analytical study, 810 employees from five organizations (in four occupational categories including administrative, technical, production, and services) completed the CMDQ, voluntarily. After collecting questionnaire data and performing relevant statistical analyses, data normalization and clustering based on the K-Means method were used to determine levels of musculoskeletal disorders. Finally, the multilayer perceptron artificial neural network was trained to predict the levels of musculoskeletal disorders and the criteria of precision, accuracy, recall and F1-score were used to evaluate the proposed model.
Results: The performance of the proposed model in predicting the levels of musculoskeletal disorders are presented in two scenarios (using and not using the SMOTE method) based on the evaluation criteria provided.
The accuracy, precision, Recall and F1-score values were 0.724, 0.709, 0.756 and 720 respectively. The appropriate accuracy and precision in the proposed model indicate its capability in identifying the levels of musculoskeletal disorders in individuals and help healthcare professionals take the necessary measures to prevent and predict them.
Conclusion: This study employs the CMDQ questionnaire and artificial intelligence methods to analyze musculoskeletal disorders in the workplace. The proposed model demonstrates significant accuracy and precision compared to similar studies. The results indicate that this model can be utilized for the identification and prediction of musculoskeletal disorders in organizational employees, offering the potential to expedite the identification process and reduce costs.
Type of Study: Research |
Subject:
Musculoskeletal Disorders
Received: 2023/12/16 | Accepted: 2024/01/30 | ePublished: 2024/01/30
Received: 2023/12/16 | Accepted: 2024/01/30 | ePublished: 2024/01/30
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
