# Assessing Fatality Risks in Maritime Accidents: The Influence of Key Contributing Factors
## 6 Discussion
Across different geographical contexts, such as the Baltic Sea, Korea, and Norway, alcohol consumption has been identified as a major contributing factor to maritime accidents, highlighting the need for stricter regulations and better safety management systems [2; 7; 8]. Various studies have focused on alcohol's direct impact on the cognitive and motor skills essential for ship navigation. From these studies it was evident that even small amounts of alcohol impair decision making, coordination, and reaction times, significantly increasing the likelihood of accidents [3; 16]. These findings point to the urgent need for stringent alcohol regulations, especially during high-risk situations. However, most studies focus on the immediate effects of alcohol, and the authors do not examine the long-term operational risks, an area where our research aims to contribute new insights. On a broader level, human factors play a critical role in maritime accidents. Errors in judgment, lapses in situational awareness, and leadership failures are all common contributors [4; 10]. While existing research covers these general factors, fewer studies directly link alcohol to specific accident types like groundings and falls overboard. Ourresearch fills this gap by investigating how alcohol directly contributes to these types of incidents, offering a deeper understanding of its role in maritime safety. From a regulatory point of view, proactive safety measures, such as strict alcohol testing and robust safety management systems, have been shown to effectively reduce accident rates [12; 15]. However, there is often inconsistency in how these rules are enforced across different regions. This requires more consistent, global policies. Our research highlights the need for unified international guidelines, with a particular emphasis on alcohol testing during high-risk conditions, such as nighttime operations or adverse weather. Methodologically, much of the previous research has relied on retrospective qualitative studies, which offer valuable insights but often lack the ability to predict future accidents. Our paper addresses this gap by adopting a quantitative approach, using predictive models in order to analyse the factors that lead to fatalities. By taking this approach, we provide a clearer, data-driven understanding of how alcohol contributes to maritime accidents. Logistic regression, as used in our study, allows the use of all types of variables, quantitative and qualitative, in one common model and the assessment of the influence of each predictor while controlling for the others. As a main advantage of the logistic model, we consider the interpretability of its results. Another advantage of this technique is that it does not require predictors to be normally distributed, which is particularly useful in our study, where some variables, such as weather conditions, may not follow a normal distribution. However, while we chose logistic regression and classification trees as the most appropriate methods for this study, we acknowledge that other techniques, such as discriminant analysis, neural networks, nearest neighbour, support vector machines, and others, could also be considered. Nowadays, neural networks are a popular powerful machine learning technique that can model highly complex, non-linear relationships between variables. However, they are well suited for large datasets and can excel at prediction tasks. In our case, we focused on the explanation task and the description of the relationship between the input variables and the outcome. In comparison with the techniques used in our study, which provide transparent and interpretable results, neural networks are often considered \"black boxes\". Moreover, neural networks typically require large datasets to perform optimally. Discriminant analysis is a very useful technique often used for classifying binary outcomes. Compared to logistic regression, its key assumption is that the independent variables follow a normal distribution.