A review on supervised machine learning for accident risk analysis: challenges in Malaysia
The new Fourth Industrial Revolution (IR 4.0) trend is driven by the concept of automation and artificial intelligence (AI). However, Malaysia is slightly behind Singapore in terms of adopting AI innovation among ASEAN countries. This paper aims to conduct a literature review of machine learning to...
| Main Authors: | , , , , |
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| Format: | Article |
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John Wiley & Sons
2022
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| Online Access: | http://psasir.upm.edu.my/id/eprint/100375/ |
| _version_ | 1848863308635963392 |
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| author | Choo, Boon Chong Abdul Razak, Musab Awang Biak, Dayang Radiah Mohd Tohir, Mohd Zahirasri Syafiie, S. |
| author_facet | Choo, Boon Chong Abdul Razak, Musab Awang Biak, Dayang Radiah Mohd Tohir, Mohd Zahirasri Syafiie, S. |
| author_sort | Choo, Boon Chong |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | The new Fourth Industrial Revolution (IR 4.0) trend is driven by the concept of automation and artificial intelligence (AI). However, Malaysia is slightly behind Singapore in terms of adopting AI innovation among ASEAN countries. This paper aims to conduct a literature review of machine learning to overcome subjectivity and bias in risk ranking decision-making. An introduction to machine learning concerning accident risk analysis is presented, and the challenges of its application in Malaysia are discussed. Existing machine learning features were evaluated to identify the feasible application in industrial accident analysis and ensure safety decision-making consistency. This review observed how the IR 4.0 approaches were used in the risk analysis, especially on supervised machine learning. This study also highlights the finding from the previous works on challenges in utilizing supervised machine learning, which is the need to have publicly accessible large database from industries and agencies such as the Department of Occupational Safety and Health (DOSH) Malaysia for the development of algorithms, which can potentially improve accident risk analysis and safety, especially for Malaysian industries. |
| first_indexed | 2025-11-15T13:30:51Z |
| format | Article |
| id | upm-100375 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T13:30:51Z |
| publishDate | 2022 |
| publisher | John Wiley & Sons |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1003752023-12-26T04:11:42Z http://psasir.upm.edu.my/id/eprint/100375/ A review on supervised machine learning for accident risk analysis: challenges in Malaysia Choo, Boon Chong Abdul Razak, Musab Awang Biak, Dayang Radiah Mohd Tohir, Mohd Zahirasri Syafiie, S. The new Fourth Industrial Revolution (IR 4.0) trend is driven by the concept of automation and artificial intelligence (AI). However, Malaysia is slightly behind Singapore in terms of adopting AI innovation among ASEAN countries. This paper aims to conduct a literature review of machine learning to overcome subjectivity and bias in risk ranking decision-making. An introduction to machine learning concerning accident risk analysis is presented, and the challenges of its application in Malaysia are discussed. Existing machine learning features were evaluated to identify the feasible application in industrial accident analysis and ensure safety decision-making consistency. This review observed how the IR 4.0 approaches were used in the risk analysis, especially on supervised machine learning. This study also highlights the finding from the previous works on challenges in utilizing supervised machine learning, which is the need to have publicly accessible large database from industries and agencies such as the Department of Occupational Safety and Health (DOSH) Malaysia for the development of algorithms, which can potentially improve accident risk analysis and safety, especially for Malaysian industries. John Wiley & Sons 2022-02-23 Article PeerReviewed Choo, Boon Chong and Abdul Razak, Musab and Awang Biak, Dayang Radiah and Mohd Tohir, Mohd Zahirasri and Syafiie, S. (2022) A review on supervised machine learning for accident risk analysis: challenges in Malaysia. Process Safety Progress, 41 (spec. 1). 147 - 158. ISSN 1066-8527; ESSN: 1547-5913 https://aiche.onlinelibrary.wiley.com/doi/abs/10.1002/prs.12346 10.1002/prs.12346 |
| spellingShingle | Choo, Boon Chong Abdul Razak, Musab Awang Biak, Dayang Radiah Mohd Tohir, Mohd Zahirasri Syafiie, S. A review on supervised machine learning for accident risk analysis: challenges in Malaysia |
| title | A review on supervised machine learning for accident risk analysis: challenges in Malaysia |
| title_full | A review on supervised machine learning for accident risk analysis: challenges in Malaysia |
| title_fullStr | A review on supervised machine learning for accident risk analysis: challenges in Malaysia |
| title_full_unstemmed | A review on supervised machine learning for accident risk analysis: challenges in Malaysia |
| title_short | A review on supervised machine learning for accident risk analysis: challenges in Malaysia |
| title_sort | review on supervised machine learning for accident risk analysis: challenges in malaysia |
| url | http://psasir.upm.edu.my/id/eprint/100375/ http://psasir.upm.edu.my/id/eprint/100375/ http://psasir.upm.edu.my/id/eprint/100375/ |