## 4 The Proposed Hybrid Driver Injury Severity Prediction Model  
Figure 2 shows the proposed approach and the experimental stages, which are data preprocessing, feature selection, driver injury severity prediction, and evaluation. In the raw dataset obtained from the GDS, dirty data were cleaned to avoid adversely affecting the performance of the model. Categorical labeling was performed on the cleaned dataset, the data were converted into a numerical format, and compatibility with DNN was ensured. To prevent the imbalance of the killed/injured/uninjured class distribution in the dataset from biasing the model against the majority class, the SMOTE-ENN oversampling technique was applied to ensure a balanced distribution among all classes. The preprocessing steps of the dataset were completed, and the relevant inputs of the model were obtained by ranking the important features according to the importance scores of the attributes, which were determined using ETC. In this way, the learning efficiency was increased and noise was reduced. For the processed dataset consisting of features that are important for the model, the DNN model, which started with an input layer consisting of 512 neurons with multiple layers, received the feature vectors and transmitted them to the next layers. The information received from the input layer was processed in three hidden layers of 256, 128, and 64 neurons, gradually reducing the dimensionality through feature extraction while maintaining the learning capacity of the model. In the DNN model, each layer used the Rectified Linear Unit (ReLU) activation function, and a regularization factor of L2 = 0.01 was applied to prevent overfitting. After each layer, batch normalization was applied to normalize the activations of the previous layer and increase the convergence speed of the model. Thus, the internal covariate drift was reduced and the training process was stabilized, allowing the model to generalize better. However, to reduce overfitting, dropout was applied after each Batch Normalization layer [24] to randomly deactivate a fraction of neurons in each training iteration and learn robust representations. Dropout rates varied between 0.4 and 0.2 across the layers. The highest dropout rate was applied after the input layer, whereas the other layers had dropout rates of 0.2. The final output layer, Output, corresponds to the three target classes (i.e., killed, injured, and uninjured) in the multi-class classification problem and consists of three neurons. A softmax function  
Figure 1: Gini importance values of dataset attributes. was used to ensure that the model outputs a probability distribution over the classes and is suitable for multi-class problems. To prevent overfitting during the training of the model and to achieve the appropriate learning rate, a Learning Rate Scheduler [25] was defined, and the Cosine Annealing [26] function and early stopping were defined as schedulers if the validation loss did not improve in a maximum of 50 epochs. In this DNN model, the model's complexity and generalization are balanced to achieve a robust performance. For the proposed hybrid multi-class approach, the hidden layers of the DNN model were treated as feature extractors, and the features learned in the layers were passed to the RF classifier for later use. Further refinement, better classification, and better generalization were achieved. Then, to validate and evaluate the performance of the model, a 5-fold 3 Repeated Stratified K-Fold [27] was applied. This is an advanced cross-validation method used to evaluate ML models, particularly for imbalanced datasets. It extends the basic K-Fold cross-validation by ensuring that each fold maintains the same class distribution as the entire dataset (stratification), and then repeats the process multiple times with different random splits to enhance the reliability and robustness of performance evaluation.