# Towards Automatic Detection of Social Anxiety Disorder via Gaze Interaction
## 5 Conclusions, Implications, and Future Directions  
This study investigated the possibility of automatic social anxiety disorder (SAD) prediction. The experiments were conducted in a live environment with human participants by using a custom-developed application. The application used the Haar Cascade classifier. SAD classes were predicted by using decision tree classifiers from the created gaze dataset. The results proved that SAD could be predicated with an overall accuracy of 80%. Furthermore, each class of SAD could be predicted with accuracies of 80%, 70%, 90%, 80%, and 80%, respectively. The possibility of replacing a subjective scale (LSAS) with an objective technique used in this research is discussed. The results suggest that individuals with SAD make lower gaze interactions during a social situation conversation. The results also suggest that as SAD class severity increases, the gaze interaction decreases. This study contributed in the following ways: (1) detected gaze interaction automatically in a real-time conversation using a simple RGB camera, (2) annotation (labeling gaze in real-time) is done in terms of gaze interaction and gaze avoidance to find the relationship between SAD and gaze interaction, (3) pioneered in finding a relationship between gaze interaction and SAD classes, (4) we created first gaze dataset that could be used in future analyses of SAD individually or along with a combination of some other SAD symptoms. Findings from the current study provide further support that individuals with elevated SAD are likely to engage in lower gaze interaction, which may have negative impacts on social functioning and contribute to the maintenance of SAD symptoms. Importantly, this support was derived using several novel methodological approaches that addressed weaknesses from earlier studies extending the external and ecological validity of this body of research. Our results confirmed that the larger trait of social anxiety severity was  
Figure 10: Confusion matrix of dataset. Class 0, 1, 2, 3, and 4 stand for ‘No SAD’, Marked, Moderate, Severe, and Very Severe groups, respectively. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Class** & **Recall** & **Precision** \\ \hline
0—No SAD & 0.80 & 0.73 \\
1—Marked & 0.70 & 0.88 \\
2—Moderate & 0.90 & 0.75 \\
3—Severe & 0.80 & 0.80 \\
4—Very Severe & 0.80 & 0.89 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Recall, precision score of SAD classes. inversely associated with the frequency and duration of gaze interaction throughout the totality of a conversation task. Finally, our results supply implications for psychotherapy. Given that gaze interaction is an essential part of meaningful human interaction, actively targeting improvement in gaze interaction as a part of SAD treatment will likely increase the quality of life for SAD patients. It is also essential to consider the nature of the social stimulus (e.g., positive; ambiguous), duration, and CBT skill development stage when preparing in vivo exposures to boost positive corrective experiences and/or successful self-regulation. The results of the current study should be viewed considering its limitations. The results of the study revealed that the female participants showed \"looking below\" behavior more than their male counterparts in each class, i.e., 5% as compared to 4.6% in the \"No SAD\" class, 11.2% vs. 7.4% in the \"Marked\" class, 16.4% vs. 14% in the \"Moderate\" class, 35% vs. 20% in the \"Severe\" class, and 35.5% vs. 20.4% in the \"Very Severe\" class. Therefore, there is a possibility of future research that decides whether this difference is a natural difference across the world, or if it is more associated with Asian culture generally and specifically in the Muslim world. Furthermore, there is a need to separate SAD from traditional shyness that is part of the eastern and Muslim cultures. A relationship between gaze interaction with SAD classes was found with 50 participants (10 in each class).