A Multiple Classification Model for the Prediction of Work-Related Musculoskeletal Disorder

Amadi Chimeremma Sandra; John-Otumu Adetokunbo Macgregor; Eze Peter Uchenna

doi:doi:10.11648/j.ijiis.20221101.11

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A Multiple Classification Model for the Prediction of Work-Related Musculoskeletal Disorder

Amadi Chimeremma Sandra, John-Otumu Adetokunbo Macgregor, Eze Peter Uchenna

Published in International Journal of Intelligent Information Systems (Volume 11, Issue 1)

Received: 3 December 2021 Accepted: 23 December 2021 Published: 8 January 2022

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Abstract

In recent times, musculoskeletal disorders (MSD) represent one of the most common and expensive occupational health problems in both developed and developing countries. Work-related musculoskeletal disorders (WRMSD) are impairments that are mostly caused by the workplace and immediate environment. A two-step predictive model is introduced here using KNN and Decision tree machine learning algorithms. This model for predicting WRMSD enables for early detection and correction of upper and lower back disorders, carpal tunnel syndrome and other WRMSD disorders associated with office workers. Key informant interview technique, observation of previous methods, online repository and published related works were used in data gathering. In training the model, 80% of the dataset was used while 20% was used for testing the model to prevent overfitting using python programming language. JavaScript, Hypertext preprocessor (PHP), Hypertext Markup Language (HTML), Cascading Stylesheet (CSS) and MySQL were also used to develop the front and backend of the application. The result revealed that the proposed model had 90.44% accuracy, 92.71% Recall (sensitivity), 97.16% precision, and 94.88% F1-Score. The proposed model, however, makes it easy for multiple classifications in other to predict both present and future risk of WRMSD. Performance is estimated to have high accuracy, recall, precision and f1 score in comparison to other existing algorithms.

Published in	International Journal of Intelligent Information Systems (Volume 11, Issue 1)
DOI	10.11648/j.ijiis.20221101.11
Page(s)	1-6
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2022. Published by Science Publishing Group

Keywords

WRMSD, Decision Tree, K-NN, Classification, Machine Learning, Predictive Model

References

[1]	Amare, D. (2010). Practice of office ergonomics on the performance of employee.
[2]	Sataloff, R. T., Johns, M. M., & Kost, K. M. (2010). Practical and easy-to-implement solutions for improving safety, health and working conditions.
[3]	McCauley-Bush, P. (2011). Ergonomics: Foundational Principles, Applications, and Technologies.
[4]	Gupta, G., Gupta, A., Mohammed, T., & Bansal, N. (2014). Ergonomics in Dentistry. International Journal of Clinical Pediatric Dentistry, 7 (1), 30–34. https://doi.org/10.5005/jp-journals-10005-1229
[5]	Kirkhorn, S. R., Earle-Richardson, G., & Banks, R. J. (2010). Ergonomic risks and musculoskeletal disorders in production agriculture: Recommendations for effective research to practice. Journal of Agromedicine, 15 (3), 281–299. https://doi.org/10.1080/1059924X.2010.488618
[6]	Pascal, S. A., & Naqvi, S. (2008). An investigation of ergonomics analysis tools used in industry in the identification of work-related musculoskeletal disorders. International Journal of Occupational Safety and Ergonomics, 14 (2), 237–245. https://doi.org/10.1080/10803548.2008.11076755
[7]	Okezue, O. C., Anamezie, T. H., John, J. N., & John, D. O. (2020). Work-Related Musculoskeletal Disorders among Office Workers in Higher Education Institutions: A Cross-Sectional Study. Ethiopian Journal of Health Sciences, 30 (5).
[8]	Nath, N. D., Chaspari, T., & Behzadan, A. H. (2018). Automated ergonomic risk monitoring using body-mounted sensors and machine learning. Advanced Engineering Informatics, 38, 514–526. https://doi.org/10.1016/j.aei.2018.08.020
[9]	Chandna, P., Deswal, S., & Pal, M. (2010). Semi-Supervised Learning Based Prediction of Musculoskeletal Disorder Risk. In Journal of Industrial and Systems Engineering (Vol. 3, Issue 4).
[10]	Suárez, S.. A., Iglesias-Rodríguez, F. J., Riesgo, F.. P., & de Cos Juez, F. J. (2014). Applying the K-nearest neighbor technique to the classification of workers according to their risk of suffering musculoskeletal disorders. International Journal of Industrial Ergonomics, 52, 92–99. https://doi.org/10.1016/j.ergon.2015.09.012
[11]	Isabel, L. N. (2012). Ergonomics: A Systems Approach. Intech Open Access Publisher.
[12]	Sasikumar, V., & Second, C. (2018). A model for predicting the risk of musculoskeletal disorders among computer professionals. International Journal of Occupational Safety and Ergnomics, 0 (0), 1–34. https://doi.org/10.1080/10803548.2018.1480583
[13]	Thanathornwong, B., Suebnukarn, S., Songpaisan, Y., & Ouivirach, K. (2014). Computer Methods in Biomechanics and Biomedical Engineering A system for predicting and preventing work-related musculoskeletal disorders among dentists. Computer Methods in Biomechanics and Biomedical Engineering, 17 (2), 177–185. https://doi.org/10.1080/10255842.2012.672565
[14]	Chander, D. S., & Cavatorta, M. P. (2017). International Journal of Industrial Ergonomics. International Journal of Industrial Ergonomics, 57, 32–41. https://doi.org/10.1016/j.ergon.2016.11.007
[15]	Schaub, K., Caragnano, G., & Bruder, R. (2012). \The European Assembly Worksheet. January. https://doi.org/10.1080/1463922X.2012.678283
[16]	Diego-mas, J. A., & Alcaide-marzal, J. (2013). Using Kinect Ô sensor in observational methods for assessing postures at work. Applied Ergonomics.
[17]	Ali, A. (2016). A Predictive Model To Identify Caregivers At Risk Of Musculoskeletal Disorders. Electronic Theses and Dissertations, 0 (0), 2004–2019.
[18]	Jagadish, R., & Qutubuddin, S.. (2018). Ergonomic Risk Assessment of Working Postures in Small Scale Industries Ergonomic Risk Assessment of Working Postures in Small Scale Industries. Grenze International.
[19]	Ribeiro, T., Serranheira, F., & Loureiro, H. (2017). Work related musculoskeletal disorders in primary health care nurses. Applied Nursing Research, 33, 72–77. https://doi.org/10.1016/j.apnr.2016.09.003
[20]	Panat, A. V, Avadhut Kulkarni, D., & Ghooi, R. (2017). Low Back Pain and Other Work Related Musculoskeletal Disorders and Choice of Treatment among Farmers in a Small Village of the Maharashtra State in India: A Self Reported Preliminary Study Using a Simple Questionnaire. International Journal of Health Sciences & Research (Www.Ijhsr.Org), 7, 11. www.ijhsr.org.
[21]	Dagne, D., Abebe, S. M., & Getachew, A. (2020). Work-related musculoskeletal disorders and associated factors among bank workers in Addis Ababa, Ethiopia: A cross-sectional study. Environmental Health and Preventive Medicine, 25 (1). https://doi.org/10.1186/s12199-020-00866-5
[22]	Abledu, J. K., Offei, E. B., & Abledu, G. K. (2014). Predictors of Work-Related Musculoskeletal Disorders among Commercial Minibus Drivers in Accra Metropolis, Ghana. Advances in Epidemiology, 2014, 1–5. https://doi.org/10.1155/2014/384279

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APA Style

Amadi Chimeremma Sandra, John-Otumu Adetokunbo Macgregor, Eze Peter Uchenna. (2022). A Multiple Classification Model for the Prediction of Work-Related Musculoskeletal Disorder. International Journal of Intelligent Information Systems, 11(1), 1-6. https://doi.org/10.11648/j.ijiis.20221101.11

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ACS Style

Amadi Chimeremma Sandra; John-Otumu Adetokunbo Macgregor; Eze Peter Uchenna. A Multiple Classification Model for the Prediction of Work-Related Musculoskeletal Disorder. Int. J. Intell. Inf. Syst. 2022, 11(1), 1-6. doi: 10.11648/j.ijiis.20221101.11

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AMA Style

Amadi Chimeremma Sandra, John-Otumu Adetokunbo Macgregor, Eze Peter Uchenna. A Multiple Classification Model for the Prediction of Work-Related Musculoskeletal Disorder. Int J Intell Inf Syst. 2022;11(1):1-6. doi: 10.11648/j.ijiis.20221101.11

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@article{10.11648/j.ijiis.20221101.11,
  author = {Amadi Chimeremma Sandra and John-Otumu Adetokunbo Macgregor and Eze Peter Uchenna},
  title = {A Multiple Classification Model for the Prediction of Work-Related Musculoskeletal Disorder},
  journal = {International Journal of Intelligent Information Systems},
  volume = {11},
  number = {1},
  pages = {1-6},
  doi = {10.11648/j.ijiis.20221101.11},
  url = {https://doi.org/10.11648/j.ijiis.20221101.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.20221101.11},
  abstract = {In recent times, musculoskeletal disorders (MSD) represent one of the most common and expensive occupational health problems in both developed and developing countries. Work-related musculoskeletal disorders (WRMSD) are impairments that are mostly caused by the workplace and immediate environment. A two-step predictive model is introduced here using KNN and Decision tree machine learning algorithms. This model for predicting WRMSD enables for early detection and correction of upper and lower back disorders, carpal tunnel syndrome and other WRMSD disorders associated with office workers. Key informant interview technique, observation of previous methods, online repository and published related works were used in data gathering. In training the model, 80% of the dataset was used while 20% was used for testing the model to prevent overfitting using python programming language. JavaScript, Hypertext preprocessor (PHP), Hypertext Markup Language (HTML), Cascading Stylesheet (CSS) and MySQL were also used to develop the front and backend of the application. The result revealed that the proposed model had 90.44% accuracy, 92.71% Recall (sensitivity), 97.16% precision, and 94.88% F1-Score. The proposed model, however, makes it easy for multiple classifications in other to predict both present and future risk of WRMSD. Performance is estimated to have high accuracy, recall, precision and f1 score in comparison to other existing algorithms.},
 year = {2022}
}

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TY  - JOUR
T1  - A Multiple Classification Model for the Prediction of Work-Related Musculoskeletal Disorder
AU  - Amadi Chimeremma Sandra
AU  - John-Otumu Adetokunbo Macgregor
AU  - Eze Peter Uchenna
Y1  - 2022/01/08
PY  - 2022
N1  - https://doi.org/10.11648/j.ijiis.20221101.11
DO  - 10.11648/j.ijiis.20221101.11
T2  - International Journal of Intelligent Information Systems
JF  - International Journal of Intelligent Information Systems
JO  - International Journal of Intelligent Information Systems
SP  - 1
EP  - 6
PB  - Science Publishing Group
SN  - 2328-7683
UR  - https://doi.org/10.11648/j.ijiis.20221101.11
AB  - In recent times, musculoskeletal disorders (MSD) represent one of the most common and expensive occupational health problems in both developed and developing countries. Work-related musculoskeletal disorders (WRMSD) are impairments that are mostly caused by the workplace and immediate environment. A two-step predictive model is introduced here using KNN and Decision tree machine learning algorithms. This model for predicting WRMSD enables for early detection and correction of upper and lower back disorders, carpal tunnel syndrome and other WRMSD disorders associated with office workers. Key informant interview technique, observation of previous methods, online repository and published related works were used in data gathering. In training the model, 80% of the dataset was used while 20% was used for testing the model to prevent overfitting using python programming language. JavaScript, Hypertext preprocessor (PHP), Hypertext Markup Language (HTML), Cascading Stylesheet (CSS) and MySQL were also used to develop the front and backend of the application. The result revealed that the proposed model had 90.44% accuracy, 92.71% Recall (sensitivity), 97.16% precision, and 94.88% F1-Score. The proposed model, however, makes it easy for multiple classifications in other to predict both present and future risk of WRMSD. Performance is estimated to have high accuracy, recall, precision and f1 score in comparison to other existing algorithms.
VL  - 11
IS  - 1
ER  -

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Author Information

Amadi Chimeremma Sandra

Department of Information Technology, Federal University of Technology, Owerri, Nigeria
John-Otumu Adetokunbo Macgregor

Department of Information Technology, Federal University of Technology, Owerri, Nigeria
Eze Peter Uchenna

Department of Computiing and Information Systems, University of Melbourne, Victoria, Australia

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Plain Text BibTeX RIS

APA Style

Amadi Chimeremma Sandra, John-Otumu Adetokunbo Macgregor, Eze Peter Uchenna. (2022). A Multiple Classification Model for the Prediction of Work-Related Musculoskeletal Disorder. International Journal of Intelligent Information Systems, 11(1), 1-6. https://doi.org/10.11648/j.ijiis.20221101.11

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ACS Style

Amadi Chimeremma Sandra; John-Otumu Adetokunbo Macgregor; Eze Peter Uchenna. A Multiple Classification Model for the Prediction of Work-Related Musculoskeletal Disorder. Int. J. Intell. Inf. Syst. 2022, 11(1), 1-6. doi: 10.11648/j.ijiis.20221101.11

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AMA Style

Amadi Chimeremma Sandra, John-Otumu Adetokunbo Macgregor, Eze Peter Uchenna. A Multiple Classification Model for the Prediction of Work-Related Musculoskeletal Disorder. Int J Intell Inf Syst. 2022;11(1):1-6. doi: 10.11648/j.ijiis.20221101.11

Copy | Download

@article{10.11648/j.ijiis.20221101.11,
  author = {Amadi Chimeremma Sandra and John-Otumu Adetokunbo Macgregor and Eze Peter Uchenna},
  title = {A Multiple Classification Model for the Prediction of Work-Related Musculoskeletal Disorder},
  journal = {International Journal of Intelligent Information Systems},
  volume = {11},
  number = {1},
  pages = {1-6},
  doi = {10.11648/j.ijiis.20221101.11},
  url = {https://doi.org/10.11648/j.ijiis.20221101.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.20221101.11},
  abstract = {In recent times, musculoskeletal disorders (MSD) represent one of the most common and expensive occupational health problems in both developed and developing countries. Work-related musculoskeletal disorders (WRMSD) are impairments that are mostly caused by the workplace and immediate environment. A two-step predictive model is introduced here using KNN and Decision tree machine learning algorithms. This model for predicting WRMSD enables for early detection and correction of upper and lower back disorders, carpal tunnel syndrome and other WRMSD disorders associated with office workers. Key informant interview technique, observation of previous methods, online repository and published related works were used in data gathering. In training the model, 80% of the dataset was used while 20% was used for testing the model to prevent overfitting using python programming language. JavaScript, Hypertext preprocessor (PHP), Hypertext Markup Language (HTML), Cascading Stylesheet (CSS) and MySQL were also used to develop the front and backend of the application. The result revealed that the proposed model had 90.44% accuracy, 92.71% Recall (sensitivity), 97.16% precision, and 94.88% F1-Score. The proposed model, however, makes it easy for multiple classifications in other to predict both present and future risk of WRMSD. Performance is estimated to have high accuracy, recall, precision and f1 score in comparison to other existing algorithms.},
 year = {2022}
}

Copy | Download

TY  - JOUR
T1  - A Multiple Classification Model for the Prediction of Work-Related Musculoskeletal Disorder
AU  - Amadi Chimeremma Sandra
AU  - John-Otumu Adetokunbo Macgregor
AU  - Eze Peter Uchenna
Y1  - 2022/01/08
PY  - 2022
N1  - https://doi.org/10.11648/j.ijiis.20221101.11
DO  - 10.11648/j.ijiis.20221101.11
T2  - International Journal of Intelligent Information Systems
JF  - International Journal of Intelligent Information Systems
JO  - International Journal of Intelligent Information Systems
SP  - 1
EP  - 6
PB  - Science Publishing Group
SN  - 2328-7683
UR  - https://doi.org/10.11648/j.ijiis.20221101.11
AB  - In recent times, musculoskeletal disorders (MSD) represent one of the most common and expensive occupational health problems in both developed and developing countries. Work-related musculoskeletal disorders (WRMSD) are impairments that are mostly caused by the workplace and immediate environment. A two-step predictive model is introduced here using KNN and Decision tree machine learning algorithms. This model for predicting WRMSD enables for early detection and correction of upper and lower back disorders, carpal tunnel syndrome and other WRMSD disorders associated with office workers. Key informant interview technique, observation of previous methods, online repository and published related works were used in data gathering. In training the model, 80% of the dataset was used while 20% was used for testing the model to prevent overfitting using python programming language. JavaScript, Hypertext preprocessor (PHP), Hypertext Markup Language (HTML), Cascading Stylesheet (CSS) and MySQL were also used to develop the front and backend of the application. The result revealed that the proposed model had 90.44% accuracy, 92.71% Recall (sensitivity), 97.16% precision, and 94.88% F1-Score. The proposed model, however, makes it easy for multiple classifications in other to predict both present and future risk of WRMSD. Performance is estimated to have high accuracy, recall, precision and f1 score in comparison to other existing algorithms.
VL  - 11
IS  - 1
ER  -

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