# Assessing Fatality Risks in Maritime Accidents: The Influence of Key Contributing Factors
## 5 Results
### Models Predicting Fatality in Maritime Accidents  
In this section, the results of the models predicting fatality in maritime accidents are presented, and the obtained results are interpreted. First, we introduce two simple models based on the influence of three factors--alcohol, weather, and time of day--on the occurrence of fatalities in accidents. The logistic regression model can be used to estimate the probability of a fatality occurring in an accident. The complete table of the model is shown in Table 4. The last category, weather = 6, was chosen as the reference category for the input variable weather, and the option night = 1 was chosen as the reference for the variable night. The reference category for the outcome variable is fatality = 0; therefore, the given model applies to the category outcome variable Y = 1 (fatality in an accident). Column B shows the estimated regression coefficient, while the values in the Exp(B) column can be used for interpretation. The BAC variable is statistically significant in the model at a significance level of 0.05, with a positive regression coefficient B. The Exp(B) value indicates that the higher the blood alcohol content, the higher the probability of a fatality in the accident. More specifically, with other conditions unchanged (i.e., weather and time of day), each additional unit of blood alcohol increases the probability of a fatality in an accident nearly 8.778 times. Regarding weather, all the weather types (labelled 1 to 4) have positive regression coefficients, indicating that compared to the reference weather category (labelled as 6), the probability of a fatality in an accident is several times higher with other conditions unchanged (i.e., BAC and time of day). However, given the significance of these variables in the model, only weather types 3 and 4 significantly differ from weather type 6 in their impact on the occurrence of a fatality at a significance level of 0.10. At this significance level, the occurrence of weather type 3 increases the probability of a fatality more than sixfold, and weather type 4 more than fivefold. Finally, if the accident occurred during the day, the probability of a fatality is almost half compared to an accident occurring at night with other conditions unchanged (i.e., BAC and weather). However, this effect is not statistically significant at the 0.05 level. The order of importance of predictors in the logistic regression model is shown in Figure 3.  
In this model, the variable weather is considered the most important (importance = 0.55), followed by the variable BAC (importance = 0.35), and night is in third place (importance = 0.10). To determine the appropriate threshold value for classifying a fatality in an accident, we created a histogram of the distribution of the predicted probabilities of a fatality, with colour differentiation of the actual outcome variable values (Figure 4). The histogram is normalised by colour for easier comparison of unevenly represented predicted probabilities. As shown in Figure 4, it is clear that the threshold value should be set at 0.40. Therefore, for an estimated probability lower than 0.40, the accident will be classified as non-fatal, and otherwise, for an estimated probability of 0.40 or higher, it will be classified as fatal. This shift in the threshold value from the original level of 0.50, which is most commonly used, increases the proportion of true positive predictions and decreases the model error due to the number of false negative predictions. This increases the model's sensitivity to the identification of accidents where a fatality is likely. We made this threshold value