## 4 Database Modeling  
The data used were provided by the Aviation Safety Network (ASN), which is \"a private, independent initiative created in 1996\", that \"covers accidents and safety issues with regards to airliners, military transport planes and corporate jets\" [32]. This ASN database is public, but access was granted for use in this paper for a 10 year period between 2007 and 2017, during which 1105 occurrences were extracted. This period is the same as that used in [21], predates the perturbations caused the COVID-19 pandemic, and only includes years in which current human factors policies were in use. Each point in the database has the narrative, causes, contributory factors precluding the occurrence and outcome on the aircraft produced by those failures. These include incidents (with no damage or minor damages to the aircraft and with or without minor injuries to the occupants) and accidents (with serious injuries, or loss of human life, or extensive damage to the aircraft whether or not there are injuries or fatalities). The 1105 occurrences were studied to identify causes of the most serious and undesirable situations, accidents with fatalities.  
To proceed with the analysis proposed and to factor in the human factors in aviation safety, the HFACS taxonomy is used, as its framework proved to be reliable in identifying human factors issues that were hidden, highlighting critical parts of human factor failure that required intervention, and improving data quality and quantity [33]. A human factors analysis can be performed from the contributory factors prior to the accident, relating them with either underlying conditions or probable causes that triggered or may trigger those events [9].  
From the analysis of each contributory factor, it was possible to notice different ways on how the human factors were categorized in the reports. In most cases, they simply correspond to the HFACS taxonomy; in others, either part of the probable causes, or the contributory factors are maintenance failures, handled according to the maintenance extension of HFACS (HFACS-ME) that was proposed by [34]. Table 2 shows the frequency of each of the factors found in the reports analyzed.  
Considering the objectives described above, it was possible to create two models for analysis considering the database features, as illustrated in Figure 4. They try to answer two questions:  
1. Is it possible to predict whether an incident or accident produced any fatality? This is the purpose of Model 1.
2. If an occurrence was fatal, is it possible to estimate the percentage of people killed? This is the purpose of Model 2.  
\begin{table}
\begin{tabular}{l r} \hline \hline
**Factor According to HFACS/HFACS-ME** & **Number of Cases** \\ \hline Adverse Mental State & 73 \\ Adverse Physiological State & 19 \\ Crew Resource Management & 11 \\ Dated/Uncertifired Equipment & 67 \\ Decision Error & 62 \\ Exceptional Violation & 20 \\ Fail to Correct Known Problem & 12 \\ Inaccessible & 2 \\ Inadequate Design & 42 \\ Inadequate Documentation & 36 \\ Inadequate Supervision & 63 \\ Inappropriate Operations & 76 \\ Infraction & 1 \\ Lighting & 1 \\ Operational Process & 12 \\ Perceptual Error & 131 \\ Personal Readiness & 134 \\ Physical Environment & 216 \\ Physical/Mental Limitations & 18 \\ Plan Inappropriate Operation & 1 \\ Resource Management & 9 \\ Routine & 114 \\ Routine Violation & 39 \\ Rule & 72 \\ Skill & 29 \\ Skill-Based Error & 104 \\ Supervisory Violation & 15 \\ Technological Environment & 19 \\ Training & 4 \\ Uncorrected Problem & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The HFACS and HFACS-ME manually labeled from the contributory causes of the database, with their frequency in the database.