## 6 Discussion
This difference between the two techniques is due to several factors related to the interpretability of the models, adaptability in learning, and data requirements. Fuzzy models in risk analysis are based on rules that are more easily recognizable and manageable by humans, making them more understandable compared to neural networks, where the decision-making process is harder to interpret. Regarding adaptability, fuzzy models handle uncertainty and inadequacy by incorporating linguistic rules, while neural networks adapt and learn only from data patterns. The most important of the three factors (interpretability, adaptability, and data) is the data requirement [9; 59]. Fuzzy systems can function satisfactorily with a small amount of data (or even none) and do not require extensive training, as research shows. The inherent ability of fuzzy logic to handle imprecision and uncertainty makes it particularly valuable in scenarios where data are sparse or qualitative. For example, fuzzy logic has been instrumental in scenarios where precise data are either unavailable or prohibitively expensive to obtain, such as risk assessments in mining or construction [125; 130; 136; 314; 315]. By transforming subjective expert opinions into risk models, fuzzy logic facilitates decision-making in environments characterized by high variability and complexity [107]. Its ease of application and adaptability further solidify its status as a preferred tool among researchers and practitioners. While the ability of fuzzy logic to handle incomplete and uncertain data makes it a valuable tool for assessing professional risk, its reliance on subjective membership functions and qualitative data can limit its accuracy. This can be particularly problematic in high-risk scenarios [7; 67]. In such cases, alternative methods such as machine learning or genetic algorithms, which can leverage large datasets and provide more detailed analysis, may be more suitable [61]. Additionally, the process of defining membership functions and creating rules depends heavily on expert knowledge. This subjectivity not only adds a layer of complexity but also makes the system prone to inconsistencies across different use cases or domains. Fuzzy systems also lack the ability to learn autonomously from new data, unlike machine learning algorithms, which can improve their predictions over time [48]. In contrast, neural networks require large volumes of data to function. Their training can be difficult and complex. A further significant drawback for research, as noted by researchers, is that data access is often unavailable, leading research to favor fuzzy logic. However, the application of neural networks is often constrained by the requirement for extensive datasets. Unlike fuzzy logic, which can function effectively with sparse data, neural networks rely on large volumes of well-annotated information to achieve meaningful results [56; 63]. This limitation has restricted their broader adoption in industries where data collection poses a challenge. This conclusion is confirmed by other authors [7; 10; 311]. Therefore, public access to large databases from industries and organizations, provided by official bodies such as the Ministry of Labor, is necessary for further developing and improving risk analysis and accident models in safety. Nevertheless, neural networks excel in dynamic environments where real-time risk estimations are essential, such as equipment health monitoring or worker fatigue detection [200]. In recent years, genetic algorithms have been sidelined. According to researchers and the literature review, genetic algorithms aim to optimize models related to workplace hazards. These algorithms are particularly beneficial for addressing complex multi-variable problems, such as determining optimal maintenance schedules or designing effective safety protocols [316]. The adaptability of genetic algorithms enables them to evolve solutions over time, making them ideal for industries characterized by rapidly changing conditions.