A machine learning web application for screening social anxiety disorder based on participants' emotion regulation (ML-SAD)

Social Anxiety Disorder (SAD) is called a neglected anxiety disorder since people do not realize its existence and the need to be evaluated by an expert. Thus, it is important to develop widely available self-screening systems to assess people and guide needy people for further evaluations. Consequently, in this paper, we present a machine learning-based web application to screen SAD. The web application consists of 10 multimedia scenarios with which people with SAD may have difficulty dealing. Four hundred eighty-eight subjects were asked to consider themselves in these scenarios and rank their competency in dealing with each specific situation considering three emotion regulation strategies. The participants were divided into two groups, SAD and No SAD, based on their diagnostic history of SAD and their self-assessment of their anxiety level. Then, a random forest was trained and tested to screen the subjects with SAD from the No SAD subjects with over 85% accuracy.