Techniques and Models for Addressing Occupational Risk Using Fuzzy Logic, Neural Networks, Machine Learning, and Genetic Algorithms: A Review and Meta-Analysis

Chris Mitrakas, Alexandros Xanthopoulos, Dimitrios Koulouriotis

This article aims to present a structured literature review that utilizes computational intelligence techniques, specifically fuzzy logic, neural networks, genetic algorithms, and machine learning, to assist in the assessment of workplace risk from human factors. The general aim is to highlight the existing literature on the subject, while the specific goal of the research is to attempt to answer research questions that emerge after the review and classification of the literature, which are aspects that have not previously been addressed. The methodology for retrieving relevant articles involved a keyword search in the Scopus database. The results from the search were filtered based on the selected criteria. The research spans a 40-year period, from 1984 to 2024. After filtering, 296 articles relevant to the topic were identified. Statistical analysis highlights fuzzy systems as the technique with the highest representation (163 articles), followed by neural networks (81 articles), with machine learning and genetic algorithms ranking next (25 and 20 articles, respectively). The main conclusions indicate that the primary sectors utilizing these techniques are industry, transportation, construction, and cross-sectoral models and techniques that are applicable to multiple occupational fields. An additional finding is the reasoning behind researchers’ preference for fuzzy systems over neural networks, primarily due to the availability or lack of accident databases. The review also highlighted gaps in the literature requiring further research. The assessment of occupational risk continues to present numerous challenges, and the future trend suggests that fuzzy systems and machine learning may be prominent.