# 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 & \(-\)3.07 & 1.18 & 6.77 & 0.009 & & & \\ BAC & 9.08 & 4.35 & 4.36 & 0.037 & 8777.90 & 1.74 & 4.42 \(\times\) 10\({}^{7}\) \\ Weather\_1 & 21.58 & 8520.79 & 0.00 & 0.998 & 2.36 \(\times\) 10\({}^{9}\) & 0.00 & \(\cdot\) \\ Weather\_2 & 20.67 & 0.00 & \(\cdot\) & \(\cdot\) & 9.43 \(\times\) 10\({}^{8}\) & 9.43 \(\times\) 10\({}^{8}\) & 9.43 \(\times\) 10\({}^{8}\) \\ Weather\_3 & 1.82 & 1.08 & 2.86 & 0.091 & 6.17 & 0.75 & 50.83 \\ Weather\_4 & 1.70 & 1.01 & 2.81 & 0.094 & 5.47 & 0.75 & 39.94 \\ Timedelay\_0 & \(-\)0.74 & 0.88 & 0.69 & 0.405 & 0.48 & 0.09 & 2.71 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Simple logistic regression model. adjustment because we consider the error of incorrectly predicting a fatal accident (false positive) as less serious if it does not occur, compared to predicting a non-fatal accident when a fatality actually occurs (false negative). In our view, a false negative error has less severe consequences. The classification table of the logistic regression model, along with the evaluation metrics, is presented in Table 5.  
The model correctly classified more than 81% of all accidents. It accurately predicted almost 83% of fatal accidents, and of the accidents predicted by the model as fatal, 80% were indeed fatal. The AUC (area under the curve) value under the ROC curve is 0.83, indicating  
Figure 4: Normalised histogram of predicted probabilities of fatality using logistic regression. Figure 3: Predictor importance in the simple logistic regression model. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Actual Y**} & \multicolumn{2}{c}{**Predicted Y**} & \multirow{2}{*}{**Total**} \\ \cline{2-3}  & **0** & **1** & \\ \hline
0 & 24 & 6 & 30 \\
1 & 5 & 24 & 29 \\ Total & 29 & 30 & 59 \\ \hline \hline \end{tabular}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Actual Y**} & \multirow{2}{*}{**Accuracy (\%)**} & \multirow{2}{*}{**31.4**} \\  & Sensitivity (\%) & & 82.8 \\ \multicolumn{2}{c}{Precision (\%)} & \multirow{2}{*}{**300.0**} \\ \multicolumn{2}{c}{AUC} & \multirow{2}{*}{**300.0**} \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Classification table and evaluation metrics of the simple logistic regression model. a reasonably accurate model. Then, a decision tree model using the CART technique was created. The importance of the variables in this model is shown in Figure 5.  
The most important variable in the classification tree is BAC (importance = 0.80), followed by weather (importance = 0.15), and the least important variable in this model is night (importance = 0.05). The classification tree is shown in Figure 6.  
Figure 6 presents the classification tree generated by the CART methods to predict the likelihood of fatalities in maritime accidents. This decision tree illustrates the most influential factors contributing to accident outcomes, with BAC as the primary variable. The figure shows that accidents where the BAC exceeds 0.274 are always classified as fatal (node 4), whereas accidents with BAC under 0.086 are always classified as non-fatal (node 1), indicating that high alcohol levels significantly increase the risk of fatalities. This figure also shows that additional factors like weather contribute to fatal outcomes in cases with moderate BAC levels (nodes 7 and 8). The first condition for branching is set by the BAC variable with a threshold value of 0.086. Accident cases where the responsible person had a blood alcohol level up to this value are all classified as non-fatal (the predicted category is always marked by grey colour in the tree). If BAC \(\geq\) 0.274, every such accident is classified as a fatal accident.