## 3 The Dataset
### Feature Selection  
The proposed method utilizes an embedded approach, the Extra Trees Classifier (ETC), for feature selection, which acts as an intermediate step between preprocessing and classification. Feature selection performs the identification of the most important features that contribute the most information to the prediction variable. Processing irrelevant features can reduce model accuracy and increase computational time [21].  
ETC is a classifier used for feature selection that includes the RF technique [22]. In this ensemble learning approach, multiple de-correlated Decision Trees are constructed using random subsets of features from the training data, and their outputs are aggregated to form a forest for classification. Each tree is given a random sample of \(n\) crash data features. Each tree selects the most relevant feature for splitting based on the Gini index (Gini Impurity). Feature selection is achieved by ranking these features in descending order of Gini Importance, allowing for the selection of the most informative subset, which enhances classification accuracy and reduces computational complexity.  
The Gini Importance quantifies the contribution of each feature to the model by measuring how much it reduces the impurity across all splits in a tree. At each node, the Gini index of the parent node is computed as follows [23]:  
\[Gini_{parent}=1-\sum_{i}^{c}{(\mu_{i})}^{2} \tag{3}\]  
where \(c\) represents the number of classes, and \(\mu_{i}\) is the probability of a feature being classified into a specific class. For each possible split of the feature, the weighted average Gini index of the child nodes is calculated:  
\[Gini_{children}=\frac{N_{left}}{N}Gini_{left}+\frac{N_{right}}{N}Gini_{right} \tag{4}\]  
where \(Gini_{left}\) and \(Gini_{right}\) are the \(Gini\) index of the child nodes. \(N_{left}\) and \(N_{right}\) are the number of samples in the left and right nodes. \(N\) is the total number of samples in the parent node. The \(Gini_{gain}\) for the feature is the difference between the \(Gini\) index of the parent node and the Gini index of the child nodes.  
\[Gini_{gain}=Gini_{parent}-Gini_{children} \tag{5}\]  
For each feature, the \(Gini_{gain}\)s across all splits where the feature is used are summed. This gives the Gini Importance for the feature. Features with higher Gini Importance contribute more to reducing impurity and are considered more predictive in the dataset.  
The Gini Importance of the characteristics that influence driver injury severity for each dataset are presented in Figure 1. The results showed that crash type, vehicle type, driver fault, intersection type, season, time, and road type were the most influential factors in driver injury severity. However, pavement type, road surface, weather conditions, and driver gender showed no significant impact. As these features exhibited the lowest correlation among the dataset variables, they were excluded from the model calculations.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{_Before SMOTE-ENN_} & \multicolumn{3}{c}{_After SMOTE-ENN_} \\ \hline \# of & \# of & \# of & **\# of & **\# of** & **\# of** & **\# of** & **\# of \\
**Uninjured** & **Injured** & **Samples** & **Samples** & **Uninjured** & **Unjured** & **Samples** & **\# of Killed** & **Samples** \\
**Samples** & **Samples** & **Samples** & **Samples** & **Samples** & **Samples** & **Samples** \\ \hline
6964 & 6202 & 68 & 13,234 & 6964 & 3065 & 9224 & 19,253 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data balancing using SMOTE-ENN.