# Assessing Fatality Risks in Maritime Accidents: The Influence of Key Contributing Factors
## 4 Materials and Methods
### Methodology
\begin{table}
\begin{tabular}{l l l l l l} \hline
**Variable** & **Role** & **Description** & **Type of Variable** & **Values** & **Distribution** \\ \hline \multirow{3}{*}{fatality} & \multirow{3}{*}{outcome variable} & \multirow{3}{*}{\begin{tabular}{l} indicator of fatality in accident \\ \end{tabular} } & \multirow{3}{*}{qualitative nominal} & \(Y=1\) for fatality & 9 (23.7\%) \\ \cline{5-6}  & & & & \(Y=0\) for non-fatality & 29 (76.3\%) \\ \hline \multirow{3}{*}{BAC} & \multirow{3}{*}{input variable} & \multirow{3}{*}{\begin{tabular}{l} blood alcohol content of person under influence, who caused the accident/is responsible for the process \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{l} quantitative \\ continuous \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{l} quantitative \\ continuous \\ \end{tabular} } & \(\langle 0;\,\infty\rangle\) & & \begin{tabular}{l} min = 0.058 \\ max = 0.690 \\ mean = 0.254 \\ median = 0.221 \\ st. dev. = 0.160 \\ skewness = 1.322 \\ kurtosis = 1.140 \\ \end{tabular} } \\ \hline \multirow{3}{*}{\begin{tabular}{l} weather \\ input variable \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{l} weather during the accident \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{l} weather during the \\ \end{tabular} } & \multirow{3}{*}{\
\begin{tabular}{l} qualitative ordinal \\ \end{tabular} } & \(X=1\): 7 times \\  & & & \(\{1,\,2,\,\ldots,10\}\) where \(X=1\) & \((18.4\%)\) \\  & & & & \(0.3\)–\(1.5\) m/s & \((18.4\%)\) \\  & & & & \(0.3\)–\(1.5\) m/s & \((18.4\%)\) \\  & & & & \(0.3\)–\(0.3\) m/s & \((18.4\%)\) \\  & & & & \(X=3\): 10 times \\  & & & & \(X=10\) foreting to & \((26.3\%)\) \\  & & & & hurricane & \(X=4\): 8 times \\  & & & & & (wind \(\geq 32.7\) m/s) wave & \((21.1\%)\) \\  & & & & height over 14 m (values & \(X=5\): 0 times (0\%)\) \\  & & & & resulting from a & \(X=6\): 6 times \\  & & & & combination of Beaufort & \((15.8\%)\) \\  & & & & \(12\)-point scale for wind & \(X=7\): 0 times (0\%) \\  & & & & speed and Douglas 9-point & \(X=8\): 0 times (0\%) \\  & & & & scale for sea state) & \(X=9\): 0 times (0\%) \\  & & & & & \(X=10\): 0 times (0\%) \\ \hline time of day & \multirow{2}{*}{input variable} & \multirow{2}{*}{
\begin{tabular}{l} time of day when accident happened \\ \end{tabular} } & \multirow{2}{*}{qualitative nominal} & \(X=1\) for night & 24 times (63.2\%) \\ \cline{5-6}  & & & & \(X=0\) for day & 14 times (36.8\%) \\ \hline \end{tabular}
\end{table} 
Table 2: Variables used in prediction models. whereas the interaction of daytime and good weather could reduce the likelihood of a fatality. As shown in Table 2, non-fatal accidents and fatal accidents are represented in a ratio of approximately 1:3. Therefore, in all the models, we use the oversampling method to balance the samples of non-fatal and fatal accidents. This means that cases from the smaller group of non-fatal accidents are given more weight in the model creation process to roughly equalise the size of the non-fatal accident group with the fatal accident group. This approach enables the creation of models with higher predictive performance. The performance of the models is evaluated using a classification table and the evaluation metrics calculated from it. The classification table is a four-field table that quantifies the absolute and relative numbers of correctly and incorrectly classified cases. An example of such a table is shown in Table 3.  
* \(Accuracy=\frac{TP+TN}{TP+FP+IN+FN}\) represents the overall accuracy of the model, i.e., the proportion of all correctly classified accidents, both fatal and non-fatal. * \(Sensitivity=\frac{TP}{TP+FN}\) is the proportion of correctly classified fatal accidents among all actual fatal accidents. * \(Precision=\frac{TP}{TP+FP}\) is the proportion of correctly classified fatal accidents among those accidents predicted as fatal.