# Assessing Fatality Risks in Maritime Accidents: The Influence of Key Contributing Factors
## 5 Results
### Models Predicting Fatality in Maritime Accidents
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Variable**} & \multirow{2}{*}{**B**} & \multirow{2}{*}{**Std. Error**} & \multirow{2}{*}{**Wald**} & \multirow{2}{*}{**Sig.**} & \multirow{2}{*}{**Exp(B)**} & \multicolumn{2}{c}{**95\% Confidence Interval for Exp(B)**} \\ \cline{5-8}  & & & & & & **Lower Bound** & **Upper Bound** \\ \hline Intercept & \(-47.21\) & 1191.83 & 0.001 & 0.98 & & & \\ BAC & 420.82 & 6.24 & 4546.49 & \(<\)0.01 & \(5.77\times 10^{182}\) & \(2.81\times 10^{177}\) & \(1.185\times 10^{188}\) \\ time\_1 \(\times\) weather\_4 & \(-46.27\) & 1917.83 & 4335.87 & \(<\)0.01 & \(1.24\times 10^{-20}\) & - & - & - \\ BAC \(\times\) weather\_6 & 422.16 & 4.96 & 7249.96 & \(<\)0.01 & \(4.56\times 10^{188}\) & \(7.60\times 10^{180}\) & \(2.75\times 10^{188}\) \\ BAC \(\times\) time\_0 \(\times\) weather\_3 & 421.13 & 0.00 &. & & \(1.27\times 10^{183}\) & \(1.27\times 10^{183}\) & \(1.27\times 10^{183}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Logistic regression model with interactions. Figure 7: Predictor importance in the logistic regression model with interactions. the combination of nighttime and weather of type 4 is notable because it decreases the probability of fatality by \(exp\{-46.27\}\) multiple. Table 8 presents the classification performance of the logistic regression model with interactions of variables. This model correctly classifies 88.1% of all accidents; among them, 79.3% of the fatal accidents were identified correctly by the model. If an accident was fatal, the model correctly classified it in almost 96% of cases. The final model was the CHAID tree with interactions of variables (Figure 8). This model uses only two of the variables: BAC in combination with night and BAC in combination with weather of type 3. According to Figure 8, BAC in combination with night is a more important variable, with an importance of 0.73, while the remaining importance of 0.27 belongs to the second variable. Figure 8 represents the CHAID classification tree, predicting the likelihood of fatalities in maritime accidents. The decision tree shows the most contributing factors with BAC as the primary variable in an interaction with the time of day. Figure 8 highlights specific combinations of BAC, time of day, and weather conditions, demonstrating how these interactions height the fatality risk. Both tree models, CHAID and CHART (Figure 6), help simplify complex relationships between variables and make the model's predictions more interpretable, offering a practical tool for assessing risk in maritime safety protocols. \begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Actual**} & \multicolumn{2}{c}{**Predicted**} & \multirow{2}{*}{**Total**} \\ \cline{2-3} \cline{5-5}  & **0** & **1** & & \\ \hline
0 & 29 & 1 & 30 \\
1 & 6 & 23 & 29 \\ Total & 35 & 24 & 59 \\ \hline \end{tabular}
\begin{tabular}{c c c} Accuracy [\%] & & 88.1 \\ Sensitivity [\%] & & 79.3 \\ Precision [\%] & & 95.8 \\ AUC & & 0.93 \\ \hline \end{tabular}
\end{table} 
Table 8: Classification table and evaluation metrics for logistic regression model with interactions. Figure 8: CHAID classification tree with interactions. According to the CHAID tree, the model classifies accidents with the level of BAC below 0.112 as fatal (node 2), as well as cases with BAC over 0.285 (node 5). Interestingly, the accidents with BAC between 0.27 and 0.285 are classified as non-fatal, but this represents only a small group of cases (node 4). The accidents where the combination of BAC and night has a value of zero (i.e., the cases where either BAC was 0 or the accidents happened during the day, node 1) are further classified according to the combination of BAC and the weather of type 3. Among these, the accidents that occurred during the day with a level of BAC between 0.101 and 0.253 are classified as fatal (node 8).