A review on supervised machine learning for accident risk analysis: challenges in Malaysia

• Main Authors: Choo, Boon Chong, Abdul Razak, Musab, Awang Biak, Dayang Radiah, Mohd Tohir, Mohd Zahirasri, Syafiie, S.
• Format: Article
• Published: John Wiley & Sons 2022
• Online Access: http://psasir.upm.edu.my/id/eprint/100375/; https://aiche.onlinelibrary.wiley.com/doi/abs/10.1002/prs.12346
• Institution: Universiti Putra 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 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.