# Weight Feedback-Based Harmonic MDG-Ensemble Model for Prediction of Traffic Accident Severity
## 4 Result and Performance Evaluation
### Performance Evaluation
\[\mathbf{F1}-\mathbf{Score}=\frac{2\times\mathbf{Precision}\times\mathbf{Recall}}{\mathbf{Precision}+ \mathbf{Recall}} \tag{5}\]  
In the first performance evaluation, the proposed ensemble model is compared with the models based on random forest, KNN (K-Nearest Neighbor), and XgbTree as single algorithms. Table 4 shows the results of the performance comparison. As shown in Table 4, the proposed ensemble model has a better accuracy and F1-score than the single models. Random forest and XgbTree are excellent, respectively, in recall and precision. Random forest is a model for predicting traffic accident severity accurately. In addition, XgbTree is the model in which the prediction value of traffic accident severity is most consistent with its actual value. In terms of the F1-score as the harmonic mean of recall and precision, the proposed ensemble model has the highest value. Therefore, the proposed harmonic MDG-ensemble model has better general performance than the single models. The second performance evaluation compares the performance according to the variable selection process method. Variable selection is the process of selecting independent variables that greatly influence a dependent variable. This helps to reduce the number of independent variables and thereby decreases the factors that negatively influence the analysis and reduce the modeling time. For performance evaluation, the comparison of the variable selection process and the accuracy and F1-Score were evaluated by comparing the proposed MDG, the multiple regression analysis methods, and the case without the variable selection process. Accordingly, it proves that MDG is more suitable for the selection of variables for predicting the severity of traffic accidents than other methods. This is the dependent variable for the severity of traffic accidents, and 16 variables are used as the independent variable. Accordingly, traffic accident severity prediction models \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **RandomForest** &
\begin{tabular}{c} **KNN** \\ **(K-Nearest Neighbor)** \\ \end{tabular} & **xgbTree** & **Ours** \\ \hline Accuracy & 0.9655 & 0.8364 & 0.9553 & 0.9686 \\ Recall & 0.8533 & 0.8738 & 0.9743 & 0.9446 \\ Precision & 0.9830 & 0.4462 & 0.8148 & 0.9089 \\ F1-Score & 0.9136 & 0.5907 & 0.8875 & 0.9264 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of the performance comparison. were generated using the proposed ensemble method. As a result of the variable selection process, the MDG model uses 11 independent variables and the multiple regression method uses 10 independent variables that satisfy a significant level of 0.5 or less. In addition, in the case of no variable selection process, the model uses 16 independent variables. The accuracy and F1-Score are used as the evaluation metrics for the prediction models. Figure 8 shows the result of comparing the performance of the predictive model according to the variable selection method. The vertical axis represents the accuracy and F1-Score, and the horizontal axis represents the variable selection method. As a result of the evaluation in Figure 8, when MDG is used for variable selection, the performance is evaluated as best. The non-selected model has more independent variables than the Used-MDG model thus it uses relatively higher-dimensional data. If a model is trained with high-dimensional data, there are many cases to consider. If there are variables that less influence a model's prediction value extraction, they negatively influence the prediction value extraction. In addition, the performance of the multiple regression method had the lowest evaluation. This is because the data used are categorical, so when the category of each data changes and the difference in results occur according to the change in significance level, the recall rate is evaluated as low, and accordingly, the F1-Score has the lowest evaluation.