## 4 Statistical Analysis and Results  
For the analysis of the article content and to extract data that would help to answer the research questions, statistical analysis of the results was conducted using IMB SPSS Statistics Version 25. As previously mentioned, the data were placed in a table (Table A1), which became the source for extracting the data and results. The main focus was the separation of articles based on the techniques analyzed in the present research. Specifically, the research identified one-hundred-sixty-three articles based on fuzzy logic, eighty-one articles that used neural networks, twenty-five articles that used machine learning, twenty articles that used genetic algorithms, and seven articles that used a hybrid approach, meaning a combination of two of the four techniques under study to achieve a result or reach a conclusion (Figure 2). It is clear that fuzzy logic has significant potential for development among researchers concerning worker safety. Neural networks also have a substantial number of articles addressing safety-related issues. The numbers for machine learning and genetic algorithms are smaller. Lastly, satisfactory efforts have been undertaken by some research groups to present techniques for solving worker safety issues using two techniques combined. Figure 3 illustrates the publication distribution of articles published in scientific journals annually, with the focus being on the use of the four methods for risk assessment over the past forty years. There is an upward trend, including some milestone years. The first milestone year is 2005, when the number of articles increased from single to double digits, and, in many years, these numbers more than doubled. Additionally, 2013 was the peak year, with 21 articles. Another milestone year is 2017. The period between 2015 and 2017 was particularly productive for this topic, with a large volume of articles but also significant annual fluctuations. Figure 2: Total number of articles per technique. From 2018 to the present, there has been stability in the subject under study, with a range of 10 to 12 articles annually until today (last search conducted in October 2024). It should be noted that, in the early period of research, from 1984 to 2014, efforts were employed to establish the four techniques under examination in the field of safety evaluation. The reason for the stability since 2018 lies in the shift among authors from the two dominant methods--fuzzy logic and neural networks--towards the promising future technique of machine learning. This is depicted in Figure 4 below. In Figure 4, the number of articles under review per year is presented, categorized by technique. In Figure 4, only the last 20 years are presented. The first 20 years, which are  
Figure 4: Annual distribution of published articles by technique. Figure 3: Annual distribution of published articles. not displayed, show a sparse and scattered representation of articles that do not warrant significant study. This extensive dispersion characterizes them as the early period during which the development of such techniques was initially attempted. It is observed that fuzzy logic and neural networks follow the same trend as presented in Figure 3, representing the total number of articles per year. In contrast, in the last three years, there has been a decrease in the use of neural networks and an increase in the use of the machine learning technique. A preliminary explanation for this is that machine learning incorporates elements and rules of neural networks. Furthermore, genetic algorithms first appeared in 2001, and their last mention was in 2014, after which their usage has significantly decreased over the last decade. A major reason for this decline is the progress in machine learning (ML) and neural networks. The rapid development of ML and artificial intelligence (AI) techniques has provided more efficient and alternative solutions.