Physiological Signals Based Anxiety Detection Using Ensemble Machine Learning

Generalized Anxiety disorder (GAD) is a neurological disorder that is mentioned in the Diagnostic and Statistical Manual of Mental Disorders, fifth edition (DSM-V), as well as its prevalence is also on a high note. Throughout the current situation, it's indeed necessary to identify the anxious state to investigate relevant incidents. The aim of this paper is to propose an automatic and intelligent anxiety identification system focused on physiological signals. In methodology, the dataset was collected and cleaned by null removal, duplicate removal, etc. After data pre processing, base machine learning algorithms were compared with ensemble learning models to identify better in terms of metrics.