MENTAL HEALTH ALERT SYSTEM WITH ARTIFICIAL INTELLIGENCE-A REVIEW

ATMAKURU JYOSHNA; A.VYSHNAVI

MENTAL HEALTH ALERT SYSTEM WITH ARTIFICIAL INTELLIGENCE-A REVIEW

Authors: ATMAKURU JYOSHNA, A.VYSHNAVI

Unique Paper ID: 169501
Volume: 11
Issue: 6
PageNo: 1118-1121

Keywords: Healthcare Web Application Personalized Care Depression Detection Text Analysis.

Abstract:
Depression is a prevalent mental health condition that impacts millions of people worldwide, resulting in considerable emotional, physical, and financial challenges. The importance of early detection and prompt intervention cannot be overstated, as they are vital for effective treatment and management. However, conventional diagnostic approaches typically depend on self-reported symptoms and clinical interviews, which may be subjective and susceptible to bias. Recently, the emergence of machine learning (ML) has introduced new possibilities for the automated identification of depression, promising a more objective, scalable, and timely recognition of depressive symptoms. ML methodologies can evaluate a variety of data sources, such as text, audio, and physiological signals, to identify patterns that suggest depression. For example, natural language processing (NLP) can be employed to examine textual information from social media interactions or clinical documentation, while audio analysis can reveal vocal indicators of depression from recorded speech. Furthermore, wearable sensors can collect physiological data, including heart rate variability and electro dermal activity, which may reflect mental health conditions. This paper examines recent progress in ML-driven depression detection, evaluates the advantages and drawbacks of different methodologies, and suggests a comprehensive framework that combines various data sources and feature extraction techniques to improve the accuracy and dependability of depression detection systems. Through this framework, we aspire to advance the ongoing initiatives aimed at utilizing ML for enhanced mental health screening and intervention.

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 6
PageNo: 1118-1121

MENTAL HEALTH ALERT SYSTEM WITH ARTIFICIAL INTELLIGENCE-A REVIEW

Available:https://ijirt.org/Article?manuscript=169501

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Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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