## 6 Conclusions  
This study introduces a hybrid framework integrating DNN and RF that addresses the prediction of driver injury severity in traffic crashes. The proposed approach was trained and evaluated on a dataset of 13,234 crashes, occurring over three years, that was collected from the GDS in Mersin, Turkiye (Table A1). By utilizing the deep feature extraction capabilities of DNN and the robust classification strengths of RF, the proposed approach effectively balanced precision and recall across all injury classes (uninjured, injured, or killed). Preprocessing techniques, including SMOTE-ENN for addressing data imbalance and ETC for feature selection, contributed to the model's generalization and predictive capability. The hybrid model demonstrated a superior performance, achieving an accuracy of 0.92, a macro F1-Score of 0.89, and a weighted F1-Score of 0.92 (Figure 8), outperforming standalone models (Figures 5 and 7) and other traditional ML-based methods, including kNN (Figure 4) and XGBoost (Figure 6). Figure 3 presents the OvR results, and Table 2, which summarizes previous studies, reveals that hybrid models outperform single models. The proposed DNN+RF model demonstrates a superior performance compared to existing studies in the literature, as evidenced by both the OvR results and the class-based outcomes (Figures 3-8).  
The results showed that crash type, vehicle type, driver fault, intersection type, season, time, and road type were the most important factors affecting driver injury severity. For example, run-off-road and fixed-object crashes, along with risky behaviors like speeding and not obeying traffic rules, were strongly linked to the severity of injuries. These findings highlight the importance of road safety audits, especially at crash-prone locations, to address design issues and improve safety. Intersection improvements, such as better design and control, can help reduce injuries. The type of vehicle was another key factor. Therefore, education programs for high-risk drivers, like motorcyclists, should focus on wearing protective equipment and adopting safe driving practices. The time of the crashes was also shown to be critical, as late-night crashes are often caused by driver fatigue. Educational efforts combined with stronger enforcement can help address these risks. For targeted safety improvements, seasonal risks and different road types should also be considered. These strategies can effectively apply the findings of this study to make roads safer and reduce injuries and fatalities in traffic.  
C.I.A.: conceptualization, investigation, supervision, writing--original draft. G.M.: methodology, software, validation, writing--original draft. M.O.: data curation, supervision, writing--review and editing. M.A.: supervision, writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This research did not receive any specific grants from funding agencies in the public, commercial, or not-for-profit sectors.  
Not applicable.  
The data used in this study are available upon request from the corresponding author. However, due to privacy concerns, the data cannot be made publicly available.  
We would like to acknowledge Turkjye's General Directorate of Security for providing the Mersin traffic accident data used for the analysis.  
The authors declare that they have no financial or personal relationships with other people or organizations that could inappropriately influence (bias) their work.