## 6 Discussion  
Occupational health and safety are of vital importance for organizations and businesses. Workplace risk control not only prevents accidents, significant restoration costs,and millions of lost work hours but also protects the most valuable asset: life itself. In this battle to reduce risks in the workplace, the scientific community responds in various ways. One approach that helps to solve such issues involves techniques based on computational intelligence. There are few contributions to the comprehensive literature review on research related to health and safety risk assessment in the workplace [9]. At this point in the discussion, it is deemed appropriate to highlight some limitations of the research. One major limitation is the potential inability of Scopus to display certain articles. There are reports from authors indicating that Scopus did not include certain articles in its database [7; 56; 67]. Another limitation could be the uneven distribution across various professional sectors. For example, a technique applied in one industrial sector might appear frequently in publications by different authors, while it may be underrepresented in another sector. This significantly restricts the subcategories within the study. Additionally, this research may be limited as some potentially relevant articles that did not meet the search criteria were not included in this review. Furthermore, the exclusion of non-English articles, conference papers, and studies that address hygiene-related risks could have influenced the overall perspective of the findings. For these reasons, a future study is recommended that expands the scope of the publications to identify additional scientific articles related to the subject. This literature review highlights the contribution of four computational intelligence techniques to occupational health and safety in addressing risks and hazards in the workplace. The application of computational intelligence methods such as fuzzy logic, neural networks, genetic algorithms, and machine learning has revolutionized occupational risk assessment. These techniques provide advanced tools for managing the uncertainties and complexities associated with workplace safety [184]. The abundance of models offered by researchers cover almost the entire range of the work environment. The key areas that stand out in the research are the industrial, transportation, construction, and mining sectors. The statistical analysis indicates that the sectors with the most articles reflect the diversity of subcategories within those fields. The statistical analysis in the Section 3 provides a record and depiction of the current state of the subject. It is a descriptive analysis conducted using SPSS. The results and visualizations of the statistical analysis preceded the formulation of the research questions. With a complete depiction of the subject, six research questions were developed. From the analysis of the first three research questions, the broad scope and complexity of the topic become evident as there are numerous subcategories for the four techniques, which are listed in Table 7, with their acronyms provided in Appendix A. This fragmentation of each technique highlights the necessity of addressing issues on a case-by-case basis for each sector and field. Analyzing Table 7, we observe how these sectors adopt various techniques from different authors. This variation exists because the complexity within each sector, and the enterprises and organizations they represent, differ. Additionally, each sector encompasses distinct content types. The analysis of the articles on the specific topic of workplace risk analysis from human factors reveals relative stability in the number of published articles per year over the past five years. The research shows that most of the articles use techniques based on fuzzy logic (163 articles), while half as many articles use neural network techniques (81 articles).