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Prediction of Work-Related Risk Factors among Bus Drivers Using Machine Learning
A recent development in ergonomics research is using machine learning techniques for risk assessment and injury prevention. Bus drivers are more likely than other workers to suffer musculoskeletal diseases because of the nature of their jobs and their working conditions (WMSDs). The basic idea of th...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9690356/ https://www.ncbi.nlm.nih.gov/pubmed/36429898 http://dx.doi.org/10.3390/ijerph192215179 |
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author | Hanumegowda, Pradeep Kumar Gnanasekaran, Sakthivel |
author_facet | Hanumegowda, Pradeep Kumar Gnanasekaran, Sakthivel |
author_sort | Hanumegowda, Pradeep Kumar |
collection | PubMed |
description | A recent development in ergonomics research is using machine learning techniques for risk assessment and injury prevention. Bus drivers are more likely than other workers to suffer musculoskeletal diseases because of the nature of their jobs and their working conditions (WMSDs). The basic idea of this study is to forecast important work-related risk variables linked to WMSDs in bus drivers using machine learning approaches. A total of 400 full-time male bus drivers from the east and west zone depots of Bengaluru Metropolitan Transport Corporation (BMTC), which is based in Bengaluru, south India, took part in this study. In total, 92.5% of participants responded to the questionnaire. The Modified Nordic Musculoskeletal Questionnaire was used to gather data on symptoms of WMSD during the past 12 months (MNMQ). Machine learning techniques including decision tree, random forest, and naïve Bayes were used to forecast the important risk factors related to WMSDs. It was discovered that WMSDs and work-related characteristics were statistically significant. In total, 66.75% of subjects reported having WMSDs. Various classifiers were used to derive the simulation results for the frequency of pain in the musculoskeletal systems throughout the last 12 months with the important risk variables. With 100% accuracy, decision tree and random forest algorithms produce the same results. Naïve Bayes yields 93.28% accuracy. In this study, through a questionnaire survey and data analysis, several health and work-related risk factors were identified among the bus drivers. Risk factors such as involvement in physical activities, frequent posture change, exposure to vibration, egress ingress, on-duty breaks, and seat adaptability issues have the highest influence on the frequency of pain due to WMSDs among bus drivers. From this study, it is recommended that drivers get involved in physical activities, adopt a healthy lifestyle, and maintain proper posture while driving. For any transport organization/company, it is recommended to design driver cabins ergonomically to mitigate the WMSDs among bus drivers. |
format | Online Article Text |
id | pubmed-9690356 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-96903562022-11-25 Prediction of Work-Related Risk Factors among Bus Drivers Using Machine Learning Hanumegowda, Pradeep Kumar Gnanasekaran, Sakthivel Int J Environ Res Public Health Article A recent development in ergonomics research is using machine learning techniques for risk assessment and injury prevention. Bus drivers are more likely than other workers to suffer musculoskeletal diseases because of the nature of their jobs and their working conditions (WMSDs). The basic idea of this study is to forecast important work-related risk variables linked to WMSDs in bus drivers using machine learning approaches. A total of 400 full-time male bus drivers from the east and west zone depots of Bengaluru Metropolitan Transport Corporation (BMTC), which is based in Bengaluru, south India, took part in this study. In total, 92.5% of participants responded to the questionnaire. The Modified Nordic Musculoskeletal Questionnaire was used to gather data on symptoms of WMSD during the past 12 months (MNMQ). Machine learning techniques including decision tree, random forest, and naïve Bayes were used to forecast the important risk factors related to WMSDs. It was discovered that WMSDs and work-related characteristics were statistically significant. In total, 66.75% of subjects reported having WMSDs. Various classifiers were used to derive the simulation results for the frequency of pain in the musculoskeletal systems throughout the last 12 months with the important risk variables. With 100% accuracy, decision tree and random forest algorithms produce the same results. Naïve Bayes yields 93.28% accuracy. In this study, through a questionnaire survey and data analysis, several health and work-related risk factors were identified among the bus drivers. Risk factors such as involvement in physical activities, frequent posture change, exposure to vibration, egress ingress, on-duty breaks, and seat adaptability issues have the highest influence on the frequency of pain due to WMSDs among bus drivers. From this study, it is recommended that drivers get involved in physical activities, adopt a healthy lifestyle, and maintain proper posture while driving. For any transport organization/company, it is recommended to design driver cabins ergonomically to mitigate the WMSDs among bus drivers. MDPI 2022-11-17 /pmc/articles/PMC9690356/ /pubmed/36429898 http://dx.doi.org/10.3390/ijerph192215179 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Hanumegowda, Pradeep Kumar Gnanasekaran, Sakthivel Prediction of Work-Related Risk Factors among Bus Drivers Using Machine Learning |
title | Prediction of Work-Related Risk Factors among Bus Drivers Using Machine Learning |
title_full | Prediction of Work-Related Risk Factors among Bus Drivers Using Machine Learning |
title_fullStr | Prediction of Work-Related Risk Factors among Bus Drivers Using Machine Learning |
title_full_unstemmed | Prediction of Work-Related Risk Factors among Bus Drivers Using Machine Learning |
title_short | Prediction of Work-Related Risk Factors among Bus Drivers Using Machine Learning |
title_sort | prediction of work-related risk factors among bus drivers using machine learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9690356/ https://www.ncbi.nlm.nih.gov/pubmed/36429898 http://dx.doi.org/10.3390/ijerph192215179 |
work_keys_str_mv | AT hanumegowdapradeepkumar predictionofworkrelatedriskfactorsamongbusdriversusingmachinelearning AT gnanasekaransakthivel predictionofworkrelatedriskfactorsamongbusdriversusingmachinelearning |