EVERGREEN

Joint Journal of Novel Carbon Resource Sciences and Green Asia Strategy

ISSN:2189-0420 (Print until Mar 2020)
ISSN:2432-5953 (Online)

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SCImago Journal & Country Rank
4.3
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4.3
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0.391
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1.192

Predicting Occupational Accident Risk from Textual Data: A Systematic Review of Machine Learning Application

Afrigh Fajar Rosyidiin1,2,*, Moses Laksono Singgih1, Adithya Sudiarno1
1Department of Industrial and Systems Engineering, Institut Teknologi Sepuluh Nopember, Indonesia
2Department of Safety Engineering, Politeknik Perkapalan Negeri Surabaya, Indonesia
*Author to whom correspondence should be addressed:
E-mail: 7010231004@student.its.ac.id (AFR)
Evergreen, Vol. 13, Iss. 02, pp. 611–628, 2026
DOI: 10.5109/7420072
Creative Commons Attribution 4.0 International Creative Commons Attribution 4.0 International
Received: June 05, 2025 | Revised: December 23, 2025 | Accepted: March 10, 2026 | Published: June 2026
Abstract
Occupational accident risk remains a persistent concern, especially in high-risk industries. However, the optimal use of textual data in safety analysis is still limited. This study aims to systematically review the application of machine learning (ML) and natural language processing (NLP) in predicting occupational accident risk using textual data. A systematic literature review was conducted using the PRISMA framework and PICOS criteria, with Scopus as the primary database. From 1238 initial articles, 21 were selected for in-depth analysis. The results indicate that algorithms such as Random Forest, Support Vector Machine, and Neural Networks are frequently used, achieving up to 94% accuracy when integrated with NLP techniques. This review highlights the growing potential of ML and NLP in developing proactive and data-driven occupational safety strategies. Further research is recommended to address limitations in data quality, model generalization and validation across various sectors
Keywords
machine learning; natural language processing; occupational accident; predicting; risk management
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