Classification of Anxiety Disorders using Machine Learning Methods: A Literature Review

Abstract

This paper focuses on providing a comprehensive literature review on the application of machine learning algorithms in the diagnosis of anxiety disorder, treatment response, and prediction of onset of anxiety disorder in recent years. Clinical decision support systems based on data-driven classifier design demonstrated a range of benefits for medical experts. The social media boom in the last decade and wearable sensors technology also opened doors for new opportunities to discover better support for clinical decisions. Still, there is a lot of room for improvement of the quality of diagnosis and new treatment scenarios can be used to enhance the mental health of the general population and reduction of tendencies of mental illness falling in severe outcomes such as suicides. Knowing the depression, anxiety, and mood of the population can also help better decision making at the government level