# Casualty Analysis of the Drivers in Traffic Accidents in Turkey: A CHAID Decision Tree Model
## 4 Results
### Performance Measures  
The \"Accuracy\" value of performance measure in the model is the ratio obtained by dividing the number of correctly predicted data in all classes by the total amount of data. The \"Precision\", on the other hand, is the ratio obtained by dividing the number of correctly estimated data in a class by the whole amount of data estimated for that specific class. Another performance criterion, \"Recall\", also known as \"Sensitivity\", is the ratio obtained by dividing the number of data estimated correctly in a class by the total observed amount of data in that class [46]. The following equations separately express the mathematical structure of these performance measures. \[\text{Accuracy}=\frac{DTP+UTP}{T} \tag{5}\]  
\[\text{Precision}=\frac{DTP}{DTP+UTP} \tag{6}\]  
\[\text{Recall}=\frac{DTP}{DTP+UTP} \tag{7}\]  
DTP: Desired case, correctly predicted  
UTP: Undesired case, correctly predicted  
DFP: Desired case, incorrectly predicted-FT1: False Type-1  
UFP: Undesired case, incorrectly predicted-FT2: False Type-2  
UUP: Undesired case, predicted as another undesired case-FT3: False Type-3  
T: Total number of cases  
The confusion matrix plays an important role in depicting the classifier's effectiveness thoroughly. This matrix provides a profound understanding of the internal workings of the classifier rather than the unique computation of Precision or Recall metrics. Hence, worthy insights that can guide further research and enhancements in model performance can be obtained through this process. Furthermore, the inherent structure of the data themselves can be released through the relationships unveiled by the confusion matrix between various data features and objects [47]. A confusion matrix is created by comparing the predicted class with the real case one in classification problems with more than two classes. While each matrix column represents the actual observed situation, each row represents the predicted one. In this confusion matrix, the values in the cells on the diagonal line characterise the number of correctly classified data. On the other hand, the rest signify misclassifications. Table 4 below illustrates all the obtained values as the performance measures of the model. Table 4 provides the \"confusion matrix\" of the models set up through all and the selected attributes of the drivers, along with the \"prediction accuracy\", \"class precision\", and \"class recall\" values reflecting the performance criteria of the model. The weaknesses of the classifier can be uncovered by delving into the confusion matrix providing valuable insights to enhance the model performance. Additionally, the confusion matrix may shed light on the relationships between various data features and objects, unveiling the inherent structure of the data themselves [47]. In other words, valuable indications can be obtained about the connections between classes and the labels signifying semantic meanings and concepts assigned to data instances. The success of the classification of a confusion matrix of size n\(\times\)n can be evaluated by the recall and precision values for each class individually [48]. As shown in Table 4, the predicted recall values for injured ones are given as 69.74 and 78.27 percent for all and selected attributes, respectively, indicating the success of the model developed. Since the number of data related to uninjured cases is limited compared to the injured cases, the same high-rate success could not be obtained although a reasonable level is achieved. In the study conducted by [PERSON] and [PERSON] (2019), property damage alone, injury, and fatality were selected as the casualty classes to predict accident severity by determining the related factors. [PERSON] and [PERSON] used lighting, intersection, collision, and road division type, fixed objects, speed limits, gender and type of occupant, alcohol usage, movement, and type and age of the vehicle as attributes to characterise the accidents.