Technical analysis of occupational fatal accidents in Malaysia using machine learning techniques
With the rapid economic growth of Malaysia, workplace accidents have increased drastically, according to the Department of Occupational Safety and Health (DOSH). This study aimed to determine the patterns in Malaysian workplace fatal accidents. A total of 505 fatal accident cases across 15 industrie...
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| Format: | Article |
| Language: | English |
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Institute of Electrical Engineers of Japan
2024
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| Online Access: | http://psasir.upm.edu.my/id/eprint/114706/ http://psasir.upm.edu.my/id/eprint/114706/1/114706.pdf |
| _version_ | 1848866571731075072 |
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| author | Zermane, Hanane Zermane, Abderrahim Mohd Tohir, Mohd Zahirasri |
| author_facet | Zermane, Hanane Zermane, Abderrahim Mohd Tohir, Mohd Zahirasri |
| author_sort | Zermane, Hanane |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | With the rapid economic growth of Malaysia, workplace accidents have increased drastically, according to the Department of Occupational Safety and Health (DOSH). This study aimed to determine the patterns in Malaysian workplace fatal accidents. A total of 505 fatal accident cases across 15 industries were analyzed in this study using both qualitative and quantitative methods. These fatality cases were identified and recorded by the DOSH from 2010 to 2020. The data were arranged and coded in Python and analyzed in terms of frequency analysis, Spearman’s rank order correlation, eta squared, chi-square, and Cramer’s V methods. Furthermore, neuro-linguistic programming was performed for word cloud and sentiment analyses. Finally, a light gradient-boosting machine learning model was used to further understand the causes of fatalities in Malaysia. The results showed that fatal falls from heights were the highest contributor to fatal accidents (32%, n = 161). Workers under contract were more vulnerable to fatal accidents in the construction industry (n = 324, 64%) than other workers. General workers were the most susceptible category to fatal accidents (60%, n = 302). The results from this study provide valuable insights into workplace fatal accident patterns and strategies for their prevention across industries. |
| first_indexed | 2025-11-15T14:22:43Z |
| format | Article |
| id | upm-114706 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T14:22:43Z |
| publishDate | 2024 |
| publisher | Institute of Electrical Engineers of Japan |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1147062025-01-23T08:15:12Z http://psasir.upm.edu.my/id/eprint/114706/ Technical analysis of occupational fatal accidents in Malaysia using machine learning techniques Zermane, Hanane Zermane, Abderrahim Mohd Tohir, Mohd Zahirasri With the rapid economic growth of Malaysia, workplace accidents have increased drastically, according to the Department of Occupational Safety and Health (DOSH). This study aimed to determine the patterns in Malaysian workplace fatal accidents. A total of 505 fatal accident cases across 15 industries were analyzed in this study using both qualitative and quantitative methods. These fatality cases were identified and recorded by the DOSH from 2010 to 2020. The data were arranged and coded in Python and analyzed in terms of frequency analysis, Spearman’s rank order correlation, eta squared, chi-square, and Cramer’s V methods. Furthermore, neuro-linguistic programming was performed for word cloud and sentiment analyses. Finally, a light gradient-boosting machine learning model was used to further understand the causes of fatalities in Malaysia. The results showed that fatal falls from heights were the highest contributor to fatal accidents (32%, n = 161). Workers under contract were more vulnerable to fatal accidents in the construction industry (n = 324, 64%) than other workers. General workers were the most susceptible category to fatal accidents (60%, n = 302). The results from this study provide valuable insights into workplace fatal accident patterns and strategies for their prevention across industries. Institute of Electrical Engineers of Japan 2024 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/114706/1/114706.pdf Zermane, Hanane and Zermane, Abderrahim and Mohd Tohir, Mohd Zahirasri (2024) Technical analysis of occupational fatal accidents in Malaysia using machine learning techniques. IEEJ Journal of Industry Applications, 13 (6). pp. 711-722. ISSN 2187-1094; eISSN: 2187-1108 https://www.jstage.jst.go.jp/article/ieejjia/13/6/13_24002974/_article 10.1541/ieejjia.24002974 |
| spellingShingle | Zermane, Hanane Zermane, Abderrahim Mohd Tohir, Mohd Zahirasri Technical analysis of occupational fatal accidents in Malaysia using machine learning techniques |
| title | Technical analysis of occupational fatal accidents in Malaysia using machine learning techniques |
| title_full | Technical analysis of occupational fatal accidents in Malaysia using machine learning techniques |
| title_fullStr | Technical analysis of occupational fatal accidents in Malaysia using machine learning techniques |
| title_full_unstemmed | Technical analysis of occupational fatal accidents in Malaysia using machine learning techniques |
| title_short | Technical analysis of occupational fatal accidents in Malaysia using machine learning techniques |
| title_sort | technical analysis of occupational fatal accidents in malaysia using machine learning techniques |
| url | http://psasir.upm.edu.my/id/eprint/114706/ http://psasir.upm.edu.my/id/eprint/114706/ http://psasir.upm.edu.my/id/eprint/114706/ http://psasir.upm.edu.my/id/eprint/114706/1/114706.pdf |