Framework for Behavioral Disorder Detection Using Machine Learning and Application of Virtual Cognitive Behavioral Therapy in COVID-19 Pandemic

TL;DR

This paper presents a digital framework utilizing machine learning to detect behavioral disorders like depression, anxiety, and internet addiction, and offers virtual Cognitive Behavioral Therapy to facilitate accessible, timely mental health support during COVID-19.

Contribution

It introduces a novel digital system that automates behavioral disorder detection and provides virtual therapy, reducing reliance on traditional lengthy assessments.

Findings

Effective detection of depression, anxiety, and internet addiction using machine learning.

Accessible virtual therapy guidance for users anytime and anywhere.

Potential to improve mental health treatment accessibility during pandemics.

Abstract

In this modern world, people are becoming more self-centered and unsocial. On the other hand, people are stressed, becoming more anxious during COVID-19 pandemic situation and exhibits symptoms of behavioral disorder. To measure the symptoms of behavioral disorder, usually psychiatrist use long hour sessions and inputs from specific questionnaire. This process is time consuming and sometime is ineffective to detect the right behavioral disorder. Also, reserved people sometime hesitate to follow this process. We have created a digital framework which can detect behavioral disorder and prescribe virtual Cognitive Behavioral Therapy (vCBT) for recovery. By using this framework people can input required data that are highly responsible for the three behavioral disorders namely depression, anxiety and internet addiction. We have applied machine learning technique to detect specific behavioral disorder from samples. This system guides the user with basic understanding and treatment through vCBT from anywhere any time which would potentially be the steppingstone for the user to be conscious and pursue right treatment.