## 6 Conclusions  
In this article, a model, based on data from incident and accident reports in the aviation industry, was proposed, to predict fatalities based on the cause of the accident. From the ASN database, it was possible to join existing information about contributory causes preceding the occurrence and its result for the aircraft, with more information about the phase of flight, the damaged sustained and the mortality. Since the role of human factors in safety procedures for aviation is paramount, a correlation was made applying the HFACS taxonomy. Three algorithms (MLP, RF, and AL) were proposed to create the desired models, based on previous work with the ASN database [21], and good results were reached, confirming that human factor policies and considerations remain indeed important to keep accidents and failures at bay.  
Figure 7: Results for Model 2 with AL after 50 queries performed with confusion matrix (**a**) and MLP algorithm (**b**).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type of Learning** & **Classes** & **Precision** & **Recall** & **Macro F1-Score** \\ \hline Random & Below 50\% & 0.43 & 0.18 & 0.41 \\ Forest & 50\%–\(<\)50\% & 0.21 & 0.21 & 0.41 \\ \hline Active & Below 50\% & 0.75 & 0.38 & 0.72 \\ Learning & 50\%–\(<\)50\% & 0.75 & 0.31 & 0.72 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison between RF and AL for predicting sparse labels for Model 2.  
In this already highly regulated industry, improving the safety standards is a main objective. Improving the safety in aviation is a constant and always ongoing objective for the entire aviation ecosystem. The results in this paper are expected to contribute to the identification of the main human factors that are root causes leading to fatal accidents, whether these root causes are related to maintenance or operations. The results in this paper are expected to contribute to selecting the areas where more investment and better procedures are more important, and more likely to have a positive impact in the reduction of fatal accidents.  
A first step to build on the work herein presented is to extend the database incorporating more recent data points, since supervised learning techniques struggled because of lack of data. Regarding the HFACS taxonomy and the process of labellng, it could be possible to incorporate a relation different types of human factors in failures and the corresponding contributory causes of accidents [37].  
Conceptualization, R.M. and D.V.; methodology, R.P.R.N., R.M. and D.V.; software, R.P.R.N.; validation, R.M., D.V. and L.F.F.M.S.; investigation, R.P.R.N.; resources, R.M.; data curation, R.P.R.N. and R.M.; writing--original draft preparation, R.P.R.N.; writing--review and editing, R.M. and D.V.; visualization, R.P.R.N.; supervision, R.M. and D.V.; project administration, D.V.; funding acquisition, R.M. All authors have read and agreed to the published version of the manuscript.  
This work was supported by: Foundation for Science and Technology--FCT, through IDMEC, under LAETA, project UIDB/50022/2020.  
Not applicable.  
The authors would like to acknowledge [PERSON] for kindly providing access to the Aviation Safety (ASN) Database for this research.  
The authors declare no conflict of interest.