## 4 Result and Discussion
### Examining Predictions through the Machine Learning Method  
Table 4 exhibits the distribution of the collisions in the whole dataset, as well as the number of hits into a parked vehicle based on the three classes of health status. The percentage of collisions that lead to injuries is greater when a moving vehicle hits a parked one. This implies the importance of using safety regulations and devices when first responders are working along the roadside. The available dataset for the severity of collisions comprised 25.8% of no injured and 73.8% of injured people (Table 4). Fatality class was a small part of the dataset relative to other cases and was discarded for explanations in this section. Lastly, the dataset could be divided into two remaining classes (no injury and injury). To reduce the skewness of the data distribution, data were balanced. Balancing prevents problems in training since the majority class (injury) cannot dominate the minority class (no injury) and the classifier can learn two distinct concepts properly. Downsampling the majority class (4745 of 6399) limited the calculations onto the number of records similar to the minority class (1654 of 6399). Figure 3 shows a simplified version of the applied decision tree with four levels on the downsampled dataset. Each node in the tree can be used as an indicator rule showing the occurrence of \"no injury\" (Class 1) or \"injury\" (Class 2) in a collision. The colors on each node show the final decision made by the decision tree at that level. The orange color in the tree suggests \"no injury\" while the blue color shows the \"injury\" category. Darker colors indicate higher confidence in the decision. The top node or the root node of the tree, is the starting point of the ruling procedure using this algorithm. The DT at each node asks a question that yields a true/false answer based on just one factor among all other factors. If a node comes with a verifiable condition on a factor, children nodes (sub-nodes) are expected at left and right. If the node does not come with a condition, it appears as a leaf node in the decision tree. The decisions are made when we reach the leaf node, but the algorithm can be stopped at any node and the decision can be made based on the information available at that node. The DT makes models more interpretable and helps to obtain the results with fewer factors. However, the best results which consider all criteria can be found in the leaf nodes. At the root node (Figure 3), the tree verifies a condition based on the \"person's position\" factor. The condition in this node is P_PSN (person's position) \(\leq\)12.5 (i.e., classes 11 and 12), which means a person is seated in the driver position (11) or the center of the front row of a vehicle (12) in a collision (see Table 2 for classifiers and Figure 2 for the codes). If the condition is true, the algorithm will move to the next level node at the left child, otherwise, it will go to the right child. For instance, if in a collision a person is seated at the front row, right outboard (P_PSN = 13 \(>\) 12.5) of the vehicle's seats, in the next level, the right child node will again check the position (P_PSN = 13 \(\leq\) 17). The next level will control the time of day and if C_HOUR \(\leq\)19.5 the person is injured. There is no condition at this child node, which is a leaf node. The light blue at this node indicates the level of confidence in this prediction. To calculate how much the tree is confident at this node, the twin values 41 and 54 on the node can be used. The level of confidence in the class \"Injury\" is 54/(54 + 41), which equals 57%. At each node, to find the level of confidence of \"no injury\", it is possible to divide the left number in the list of values by the sum of twin values, and the level of confidence for the \"injured\" class is the division of the right value to the sum of twin values. As mentioned earlier, although a decision can be made at each node, they are not as accurate as the leaf node.