## 4 Dataset 2 Austrian Workers' Compensation Board (AUVA)
### Machine Learning (ML) Models  
Machine Learning (ML) models such as decision trees (DTs), random forests (RFs), and neural networks (NNs) can be used to make predictions about the fatality of the accident (classification) and the number of days a patient spends being hospitalized. Those predictions are based on the learning of a non-linear relationship between the factors that are gathered to describe the phenomenon and the variable that is being predicted. If the prediction reaches a sufficiently large performance value, then the internal decision-making process of such models can provide some insights into the data that the models were trained  
Figure 11: Illustration of the Spearman correlation results for the selected features.  
Figure 12: Illustration of the estimated mutual information for the selected features.  
upon and can uncover associations between multiple influencing factors that act at the same time and not evaluate the effect of each of the factors alone (as seen in Section 4.1).  
The prediction of the number of sick leave days can be turned into a classification task when a threshold >0 days denotes an accident resulting in downtime, and anything equal to 0 hospitalization days is not considered so effective. Human domain knowledge was incorporated to choose the first six factors that were believed to be decisive for predicting the severity of accidents: time of accident, month, age, weekday, type of injury, and reason for injury. Although ML algorithms perform amazingly well today, human domain knowledge can sometimes be very important for certain tasks, and the involvement of a human expert can bring advantages [48]. Month and time had to be cyclically encoded with the use of sine and cosine functions since conditions in December were considered to be similar to the ones in January; similarly, 00:00 midnight should be encoded as 01:00. Furthermore, the type of injury and reason for injury contained textual information, which in this first stage was encoded with the simple word2 vec algorithm [49]. The resulting continuous three-dimensional embeddings had prevalence over the other input features regarding decision-making importance because of the inductive bias, which is an inherent property of several ML solutions, including decision trees (DTs) [50]. The data were augmented with a Variational Autoencoder (VAE) [51] before they were used for training. The resulting augmented dataset was imbalanced (52,007 for 0 and 12,286 for >0 sick leave days), correctly reflecting the class imbalance in the original dataset; therefore, the Adaptive Synthetic Sampling approach (ADASYN) [52] was used for minority class sampling.  
The performance of a decision tree (DT) trained on the aforementioned factors and hyperparameter search is depicted in Figure 13. The number of true negatives (TN) was 10,722, that of true positives (TP) was 10,301, that of false positives (FP) was 801, and that of false negatives (FN) was 945 [33]. The classification had an overall balanced accuracy score of 0.923, and the mutual information (MI) was 0.422 [34].  
The rules by which the DT decided whether a particular accident case should be predicted as having 0 or >0 hospitalization days are depicted in the Figure 14. For better readability, only the two first levels of the DT are presented. It is observable that the type of injury was selected as the most relevant feature for the classification, which is questionable with respect to expert domain knowledge. This phenomenon can be explained through the aforementioned inductive bias [50], and together with the fact that the decision tree contains  
Figure 13: Confusion matrix of the decision tree.  
the necessary encoding of the input features in its rules, the DT is overall a suboptimal solution for modeling forest accidents.