# Weight Feedback-Based Harmonic MDG-Ensemble Model for Prediction of Traffic Accident Severity
## 4 Result and Performance Evaluation
### Performance Evaluation
Therefore, in the process of selecting variables according to importance, the F1-Score and accuracy of the Used MDG model are improved compared to those of the non-selected and multiple regression models. In the third performance evaluation, the suitability for the application of harmonic-mean to weight feedback was evaluated, the arithmetic mean, geometric mean, and harmonic mean were applied, and their accuracy was compared according to the importance of eleven variables extracted through the MDG coefficient. Figure 9 shows the results of an accuracy comparison between the arithmetic mean, geometric mean, and harmonic mean. As shown in Figure 9, the harmonic-mean-based method is the most accurate. In the case of the arithmetic mean, since the eleven variables extracted through the MDG coefficient including time, the number of deaths, month, position, type of assaulant, year, minute, type of accident, date, law violation, and day have no common criteria, there are no criteria for division. For this reason, when the arithmetic means are applied, the accuracy is low. In addition, it is impossible to find a constant rise ratio for the eleven variables. Therefore, when the geometric mean is applied, the accuracy is lowest. When the proposed harmonic mean is applied, the balance remains, and a changing mean ratio is determined. Therefore, it exhibits the best performance. Figure 8: Results of performance comparison between the Mean Decrease Gini coefficients-based model and the non-Gini model. There is a difference in the degree of risk of an accident according to the threshold of the severity of a traffic accident. Therefore, the ROC (Receiver Operating Characteristic) curve is used to evaluate the proposed classification model according to the change of the threshold value. The ROC curve represents a curve through TPR (True Positive Rate) and FPR (False Positive Rate) at the thresholds of various classifications. In order to determine the superiority of the model through the ROC (Area Under the Curve), it is determined through the area of the AUC representing the area under the curve. AUC indicates from 0 to 1, and the larger the value, the better the performance. Figure 10 shows the ROC curve and AUC area of the proposed model. As a result of the evaluation, the area of AUC was evaluated as 0.871. In the last performance evaluation, the proposed weight feedback-based harmonic MDG-ensemble model is compared with models proposed by [PERSON] et al. [40] and [PERSON] et al. [6] to evaluate the quality of the proposed model. For the prediction of traffic accident severity, [PERSON] used the improved NSGA-2 algorithm using traffic accident data provided by the Teheran Police Department. [PERSON] used a Gaussian-Kernel SVM with the traffic accident data provided by the Ministry of Road Transport & Highways  
Figure 10: ROC (Receiver Operating Characteristic) and AUC (Area Under the Curve) area of the model. Figure 9: Results of an Accuracy Comparison Between Arithmetic Mean, Geometric Mean, and Harmonic Mean. India. Their traffic accident severity prediction results were compared using accuracy, recall, precision, and F1-Score. Table 5 shows the performance comparison between the proposed model and other models. As shown in Table 5, the proposed model has better accuracy, recall, precision, and F1-Score than the models proposed by [PERSON] and [PERSON]. [PERSON]'s model analyzes traffic accident severity according to user preference. It extracts customized knowledge for road safety experts, such as traffic policemen and road and transportation engineers. It generates rules based on user preferences. In the model, the results differ depending on the experts' data sets. Therefore, the model has a lower performance than the proposed model. [PERSON]'s model has no variable importance and variable selection processes. All analyzed data are categorical data. The Gaussian-Kernel SVM is a learning model influenced by the spatial distribution of data. [PERSON]'s model does not fit the features of the data to analyze.