# Human Factors as Predictor of Fatalities in Aviation Accidents: A Neural Network Analysis
## 5 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Type of**} & \multirow{2}{*}{**Performance**} & \multicolumn{3}{c}{**Class**} & \multicolumn{2}{c}{**Averages**} \\ \cline{3-6}  & & **No Fatality** & **Fatality** & **Macro** & **Weighted** \\ \hline \multirow{4}{*}{Multiplayer Perceptron Experiment 1} & Precision & 0.91 & 0.76 & 0.83 & 0.87 \\ \cline{2-6}  & Recall & 0.94 & 0.69 & 0.81 & 0.88 \\ \cline{2-6}  & F1-Score & 0.92 & 0.72 & 0.82 & 0.88 \\ \cline{2-6}  & Accuracy & & 0.88 & & \\ \hline \multirow{4}{*}{Multiplayer Perceptron Experiment 2} & Precision & 0.92 & 0.79 & 0.86 & 0.89 \\ \cline{2-6}  & Recall & 0.94 & 0.75 & 0.84 & 0.89 \\ \cline{2-6}  & F1-Score & 0.93 & 0.77 & 0.85 & 0.89 \\ \cline{2-6}  & Accuracy & & 0.89 & & \\ \hline \multirow{4}{*}{Multiplayer Perceptron Experiment 3} & Precision & 0.93 & 0.80 & 0.86 & 0.90 \\ \cline{2-6}  & Recall & 0.94 & 0.76 & 0.85 & 0.90 \\ \cline{2-6}  & F1-Score & 0.94 & 0.78 & 0.86 & 0.90 \\ \cline{2-6}  & Accuracy & & 0.90 & & \\ \hline \multirow{4}{*}{Multiplayer Perceptron Experiment 4} & Precision & 0.91 & 0.88 & 0.89 & 0.90 \\ \cline{2-6}  & Recall & 0.97 & 0.69 & 0.83 & 0.90 \\ \cline{2-6}  & F1-Score & 0.94 & 0.77 & 0.85 & 0.90 \\ \cline{2-6}  & Accuracy & & 0.90 & & \\ \hline \multirow{4}{*}{Multiplayer Perceptron Experiment 3} & Precision & 0.91 & 0.88 & 0.89 & 0.90 \\ \cline{2-6}  & Recall & 0.97 & 0.69 & 0.83 & 0.90 \\ \cline{2-6}  & F1-Score & 0.94 & 0.77 & 0.85 & 0.90 \\ \cline{2-6}  & Accuracy & & 0.90 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance indexes of each experiment processed via neural network. Figure 7: Confusion matrix of each experiment via neural network: (**a**) Experiment 1, (**b**) Experiment 2, (**c**) Experiment 3, and (**d**) Experiment 4. Figure 8: Confusion matrix of each experiment via random forest: (**a**) Experiment 1, (**b**) Experiment 2, (**c**) Experiment 3, and (**d**) Experiment 4. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Type of** \\ **Learning** \\ \end{tabular} } & \multirow{2}{*}{**Performance**} & \multicolumn{3}{c}{**Class**} & \multicolumn{2}{c}{**Averages**} \\ \cline{3-6}  & & **No Fatality** & **Fatality** & **Macro** & **Weighted** \\ \hline \multirow{4}{*}{\begin{tabular}{c} Random \\ Forest \\ Experiment 1 \\ \end{tabular} } & Precision & 0.91 & 0.76 & 0.83 & 0.87 \\ \cline{2-6}  & Recall & 0.94 & 0.69 & 0.81 & 0.88 \\ \cline{2-6}  & F1-Score & 0.92 & 0.72 & 0.82 & 0.88 \\ \cline{2-6}  & Accuracy & & 0.88 & & \\ \hline \multirow{4}{*}{\begin{tabular}{c} Random \\ Forest \\ Experiment 2 \\ \end{tabular} } & Precision & 0.92 & 0.79 & 0.86 & 0.89 \\ \cline{2-6}  & Recall & 0.94 & 0.75 & 0.84 & 0.90 \\ \cline{2-6}  & F1-Score & 0.93 & 0.77 & 0.85 & 0.89 \\ \cline{2-6}  & Accuracy & & 0.90 & & \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} Random \\ Forest \\ Experiment 3 \\ \end{tabular} } & Precision & 0.92 & 0.79 & 0.86 & 0.89 \\ \cline{2-6}  & Recall & 0.94 & 0.75 & 0.84 & 0.90 \\ \cline{2-6}  & F1-Score & 0.93 & 0.77 & 0.85 & 0.89 \\ \cline{2-6}  & Accuracy & & 0.90 & & \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Random \\ Forest \\ Experiment 4 \\ \end{tabular} } & Precision & 0.90 & 0.87 & 0.89 & 0.89 \\ \cline{2-6}  & Recall & 0.97 & 0.65 & 0.81 & 0.90 \\ \cline{2-6}  & F1-Score & 0.93 & 0.74 & 0.84 & 0.89 \\ \cline{2-6}  & Accuracy & & 0.90 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance indexes of each experiment processed via random forest. The results show that Experiment 2, using human factors as inputs, achieves better results than Experiment 1, the inputs of which are the three variables from the reports' metadata. It is true that Experiments 3 and 4, combining human factors with one of the three variables or with all of them, present slight improvements when compared to Experiment 2. Such improvements, however, are marginal and not consistent since for some indicators, Experiment 2 outperforms all others.