## 3 Results
### Model Comparison
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Feature** \\ **Selection** \\ \end{tabular} } & \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**SEV1 (\%)**} & \multicolumn{3}{c}{**SEV2 (\%)**} & \multicolumn{3}{c}{**SEV3 (\%)**} & \multicolumn{3}{c}{**Total (\%)**} \\ \cline{3-13}  & & **TPR** & **FPR** & **ACC 1** & **TPR** & **FPR** & **ACC & **TPR** & **FPR** & **ACC** & **ACC** & **AUC** & **TPR** \\ \hline \multirow{9}{*}{BF} & BOL & 81.67 & 35.22 & 71.94 & 23.59 & 11.24 & 71.17 & 67.77 & 14.07 & 80.37 & 63.74 & 80.80 & 20.18 \\  & MLP & 80.72 & 33.28 & 72.66 & 23.54 & **10.82 2** & 71.46 & 69.62 & 15.91 & 79.67 & 61.89 & 79.93 & 20.00 \\  & SVM & **85.09** & 35.16 & 73.44 & 24.87 & 10.92 & 71.75 & 67.54 & 11.98 & 81.76 & 62.74 & 82.17 & 19.35 \\  & RF & 81.01 & 30.47 & 74.41 & 28.71 & 13.33 & 71.03 & 71.66 & **12.52** & 82.64 & 64.04 & 82.00 & 18.77 \\  & DNN & 81.63 & 27.39 & **76.44** & 30.97 & 12.88 & **71.97** & **74.96** & 12.84 & **83.43** & **65.92** & **84.02** & **17.70** \\  & LCE & 75.29 & **25.80** & 75.23 & **35.87** & 17.77 & 0.43 & 71.80 & 13.36 & 83.10 & 63.89 & 81.12 & 18.98 \\  & Mean & 80.90 & 31.22 & 74.02 & 27.93 & 12.83 & 71.30 & 70.56 & 13.45 & 81.83 & 63.70 & 81.67 & 19.17 \\ \hline \multirow{9}{*}{PCI} & BOL & 85.68 & 35.78 & 74.83 & 20.51 & 11.07 & 71.96 & 70.39 & 11.38 & 84.54 & 71.41 & 85.51 & 19.41 \\  & MLP & 87.70 & 27.36 & 80.53 & 32.36 & 11.20 & 75.06 & 78.09 & 8.99 & 88.56 & 69.31 & 85.84 & 15.85 \\  & SVM & 89.27 & 27.94 & 80.86 & 34.51 & 10.45 & 76.11 & 79.81 & 6.88 & 90.55 & 73.55 & 89.24 & 15.09 \\  & RF & 87.11 & 19.50 & 84.80 & 48.25 & 11.78 & 78.93 & 83.88 & 6.54 & 92.03 & 77.64 & 91.30 & 12.61 \\  & DNN & 90.90 & 17.27 & 87.71 & 56.61 & 9.31 & 82.99 & 84.88 & 4.99 & 93.41 & 81.82 & 93.54 & 10.52 \\  & LCE & **92.88** & **9.43** & **93.05** & **73.84** & **7.32** & **89.09** & **90.43** & **3.23** & **96.32** & **88.98** & **97.03** & **6.66** \\  & Mean & 88.92 & 22.88 & 83.63 & 44.35 & 10.19 & 79.02 & 81.25 & 7.00 & 90.90 & 76.45 & 90.08 & 13.36 \\ \hline \multirow{9}{*}{AF} & BOL & 85.23 & 33.16 & 76.14 & 26.10 & 10.39 & 73.97 & 74.78 & 10.45 & 86.54 & 73.58 & 87.14 & 18.00 \\  & MLP & 83.79 & 29.17 & 77.83 & 30.26 & 12.40 & 73.62 & 77.14 & 9.87 & 87.66 & 71.32 & 86.91 & 17.15 \\ \cline{1-1}  & SVM & **94.68** & 27.94 & 83.16 & 37.38 & **7.15** & 79.38 & 83.97 & 3.97 & 93.84 & 77.44 & 93.27 & 13.02 \\ \cline{1-1}  & RF & 88.25 & 16.62 & 86.95 & 54.87 & 10.92 & 81.34 & 84.97 & 6.09 & 62.68 & 79.74 & 92.86 & 11.21 \\ \cline{1-1}  & DNN & 90.73 & 16.07 & 88.32 & 57.79 & 9.20 & 83.39 & 86.37 & 5.09 & 93.80 & 82.01 & 94.11 & 10.12 \\ \cline{1-1}  & LCE & 93.37 & **9.21** & **93.38** & **74.51** & 7.27 & **89.32** & **90.49** & **2.88** & **96.60** & **89.21** & **97.23** & **6.45** \\ \cline{1-1}  & Mean & 89.34 & 22.03 & 84.30 & 46.82 & 9.56 & 80.17 & 82.95 & 6.39 & 91.85 & 78.05 & 91.59 & 12.66 \\ \hline \multicolumn{11}{c}{Total (mean)} & 86.39 & 25.38 & 80.65 & 39.70 & 10.86 & 76.83 & 78.25 & 8.95 & 88.19 & 72.74 & 87.78 & 15.06 \\ \hline \hline \end{tabular}  
* ACC stands for accuracy. 2 The best performance for different categories is shown in bold.  
\end{table} 
Table 3: Model performance of the different ML methods. (high FPR) renders it an unsuitable algorithm for this dataset. (4) The LCE-based model can effectively identify the high-dimensional relationships between pre-crash information and injury level. The model accuracy is improved by 25.32% with proper feature engineering, which can be effectively used for IR assessment in pre-crash conditions. The model performance for certain severity levels considerably varies when each level is regarded as a binary variable. SEV3 exhibited more evident crash patterns and higher accuracy, while the accuracy and sensitivity are lower at SEV2 due to the ambiguous mechanism for moderate severity. In addition, the SEV1 achieves the highest average TPR (86.39%) and the highest average FPR (25.38%), and the misclassification rate can be moderated with LCE.