New Perspectives on Occupational Safety Science: A Multidimensional Review of Machine Learning Applications in Occupational Accident Analysis

Occupational accidents pose a major threat to economic enterprises in many different industries, reducing labor productivity and causing substantial economic losses, particularly in highly industrialized regions. According to ILO data, losses due to occupational accidents in Türkiye reached to 1 trillion 51 billion TL in 2023, highlighting the need for evidence-based analytical approaches to strengthen prevention strategies. Effective occupational accident analysis is essential not only for occupational safety and health (OHS) but also for sustainable development. Recent studies increasingly leverage data-driven methods, although existing findings remain fragmented and lack methodological synthesis. Alongside traditional statistical methods, modern machine learning (ML) techniques offer enhanced analytical capabilities for accident analysis. Machine learning methods, including prediction, classification, and clustering algorithms, are increasingly used to support risk identification, pattern analysis, and data-driven safety insights by learning from historical data and modeling complex accident patterns. This study systematically reviews existing research on ML applications in occupational accident analysis, examining investigated sectors, employed algorithms, data sources, and analytical trends. The review also identifies key research gaps and outlines future directions for ML-driven OHS studies.