## 3 Dataset 1: Austrian Federal Forests
### Summary and Next Step  
The study on dataset 1 investigated occupational accidents within the Austrian Federal Forest, which oversees approximately 10% of the Austrian state area [43]. Decision trees, random forests, and fully connected neural networks were utilized to predict the  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Decision Tree** & **Random Forest** & **Neural Network** \\ \hline Accuracy (a) & 0.98 & 0.98 & 0.98 \\ Mutual information (a) & 0.60 & 0.63 & 0.64 \\ Accuracy (b) & 0.99 & 1.0 & 0.99 \\ Mutual information (b) & 0.67 & 0.69 & 0.67 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the classification models with 4 (a) and 7 (b) features [4].  
Figure 6: The feature importances in the trained decision tree; on the x axis the feature importance, on the y axis the features; reprinted with permission from [4], [PERSON] 2022.  
length of absence resulting from accidents and to categorize incidents as fatal or non-fatal. The decision tree analysis revealed that younger employees and workers typically experienced shorter recovery periods, contingent upon the time of the accident. Furthermore, distinctions were evident between accidents occurring during the initial days of the week and those occurring later in the week.  
The findings underscored the significance of age, particularly concerning the prediction of recovery duration for injured individuals. These results align with prior studies [9; 44], emphasizing the importance of paying attention to the health and safety of elderly workers and employees, particularly in light of demographic shifts. Furthermore, our analysis confirmed the anticipated disparity between workers and employees, indicating that manual labor was associated with a higher frequency of severe accidents. Moreover, time emerged as a crucial factor in our models, with peaks in occupational accidents observed between 10 and 12 a.m., and a smaller peak occurring between 2 and 4 p.m. This finding echoes previous research attributing variations in accident probabilities throughout the day to factors such as fatigue and dehydration [7; 12].  
In the subsequent phase of this study, expanding the database holds the potential to enhance and extend the methodology. This expansion could involve the inclusion of additional factors such as company size, level of training, or professional backgrounds of individuals involved. Additionally, conducting comparative analyses between this dataset and data from other institutions across Europe could unveil hidden patterns or correlations. A long-term objective of this research is to develop a causal model [45; 46; 47]. It is evident that categorical variables exhibited relationships with each other; a notable example was the \"body part\" feature, where certain values encompassed accidents affecting multiple body parts, while others focused on single parts (particularly before grouping). Investigating the existence, structure, and parameters of relationships among variables such as the working sector, cause of the accident, and affected body parts will be the focus of future investigations, necessitating higher data quality requirements.