# Towards Automatic Detection of Social Anxiety Disorder via Gaze Interaction
###### Abstract  
Social anxiety disorder (SAD) is an extreme fear of underperformance in various social situations. It is necessary to detect people with or without SAD for counseling and treatment. A few manual techniques in the existing literature show the possibility of SAD detection from gaze interaction. However, an automated prediction of SAD is scarce. In this research, an automatic technique to predict SAD using gaze interaction/avoidance is proposed, where a custom application was developed that used the Haar Cascade classifier to predict gaze interaction/avoidance. The experiments were conducted on 50 participants in a live environment using the developed application. 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, four classes of SAD (Mark, Moderate, Severe, Very Severe along with 'No SAD') could be predicted with an accuracy of 80%, 70%, 90%, 80%, and 80%, respectively. The research proved the possibility to predict SAD using computer-based methods without human intervention. Furthermore, it created the possibility of aiding a subjective Liebowitz Social Anxiety Scale (LSAS) with an objective technique described in this research. social anxiety disorder (SAD); LSAS scale; automatic prediction; haar cascade classifier; decision tree classifier; SAD classes +
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normal routine. About 80% of people with SAD also experience other psychiatric conditions like specific phobia, major depressive disorder (MDD), and dependency on alcohol. The problems associated with the disorder demand proper treatment. If left untreated, SAD could cause various social negative behaviors like isolation, cyberbullying, harmful, social academic, and psychological loneliness, problems in making friends, depression, and school refusal [4]. If left untreated, it has an increased risk of persistence into adulthood after 11 years of age. There are various significant methods to treat SAD, i.e., cognitive behavioral therapy (CBT), exercising, yoga, etc. Computers and the latest machine-learning algorithms can also aid in all the above-mentioned methods either directly or indirectly. In recent years, computers have been effectively used to measure social and mental disorders. The reliance on information and communication technologies (ICT) is increasing because of the following two main reasons:  
1. To avoid the patients' fear: Despite SAD being the cause of various sociological damages, only half of the adults seek treatment [5], because they fear being negatively evaluated by a healthcare professional which, by itself, is a symptom of SAD. 2. Financial issues: SAD imposes extensive economic costs on individuals. The treatment costs are significantly higher for people that have SAD as compared to other chronic physical diseases. The studies by [PERSON] et al. [4] and [PERSON] et al. [5] analyzed the data from the Netherlands' national mental health survey and estimated an annual mean cost of 611,952 per person. In addition, literature such as [6,7] also suggests that there is a rising cost of the diagnosis of psychological disorders. The solution to mentioned problems could be solved by developing ICT-oriented applications that could automatically predict patients' SAD. There exist some examples of the use of ICTs to predict psychological disorders. For example, machine-learning algorithms are used to predict depression among senior citizens [8], an automated video-based recognition of depression and anxiety symptom severity [5], or to improve and predict the state of healthcare [9]. Machine learning is used for many other applications within social contexts, e.g., social media presence and analysis on Twitter or YouTube [10, 11, 12], or general applications of image or language processing [13, 14, 15, 16, 17]. However, research in using machine-learning techniques to predict SAD is scarce. The existing studies on the prediction of SAD mostly used gaze interaction [8,18].