# Towards Automatic Detection of Social Anxiety Disorder via Gaze Interaction
## 4 Analysis, Results, and Discussions
### Is SAD Predictable via Gaze Avoidance sub-Behaviors?  
The gaze avoidance data were further categorized into looking below, looking above, looking right, and looking left behaviors (c.f. Section 3.4.). It was hypothesized that participants with SAD would show dominant looking-down behavior. This finding could give us a possibility of detecting SAD, even from a sub-behavior of gaze avoidance. Figure 8 shows female gaze avoidance behavior further sampled into the four sub-behaviors. In the gaze avoidance category, 35%, and 34.47% of the sub behavior in'severe' and'very severe' participants were \"looking below\" behavior. Similarly, Figure 9 shows male gaze avoidance behavior, which shows that looking below behavior intensity is lesser in males, i.e., 20% and 20.4% in case of severe and'very severe' classes. However, this sub-behavior still forms major gaze avoidance behavior in males as well. A paired sample t-test conducted on the joint gender data showed a significant difference between looking below behavior and all other avoidance behaviors, as shown in Table 4. Furthermore, all the participants showed this behavior with medium or large effect size as [PERSON]'s D column in Table 4 shows. Figures 8 and 9 also show the trend that as SAD class severity increases, looking below behavior also increases, which is not the case for all other gaze avoidance sub-behaviors. This is  
to the decision tree, which classified data into five classes (one No SAD class and four SAD classes).  
The classifier was used to predict an overall accuracy as well as to predict the accuracy of the sub-classes as well. The earlier literature only differentiated between the SAD group (A group of participants that have a social anxiety disorder) and the 'No SAD' group (A group of participants that have no social anxiety disorder). In this way, the sub-class classification was a novel process executed in this research. The results show that the overall accuracy of this method was 80%. The confusion matrix of the dataset prediction of SAD is illustrated in Figure 10.  
From the confusion matrix, the precision and recall scores were calculated as illustrated in Table 5. As can be seen from the data in Table 5, the recall percentage of 'No SAD', Marked, Moderate, Severe, and very severe groups were 80%, 70%, 90%, 80%, and 80%, respectively. The third column refers to recall, i.e., the percentage of total relevant results of each class that is correctly classified by the algorithm. The precision scores of 'No SAD', Marked, Moderate, Severe, and very severe groups were 73%, 88%, 75%, 80%, and 89%, respectively.  
Figure 8: Comparison between gaze avoidance behavior of females.  
Figure 9: Comparison between gaze avoidance behavior of males.  
These results show that there is a possibility of replacing a subjective LSAS scale of SAD prediction with an objective method defined in this research with an accuracy of 80%. As shown earlier (c.f. Section 2--Related Work), the subjective scale has many disadvantages, including a biased response from the participants that could affect the accuracy of the SAD prediction with the LSAS scale. There is a possibility of replacing that scale with the process described in this research. Otherwise, the process could be used along with the LSAS scale for a more correct assessment and prediction of SAD.