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Abstract

Nowadays, people are becoming more and more concerned with their physical health, but mental health is not given the same level of attention. Even if they are aware that they have been afflicted by chronic mental illnesses, many people choose not to seek treatment out of fear of what others would think, a belief that they have lost their minds, a dislike of doctors, or all three. These circumstances make it urgently necessary to find a solution so that more individuals are not inclined to mental diseases. This paper focuses on forecasting mental health using deep learning approaches and machine learning algorithm that is support vector machine. Support vector machine is used to solve the existing problem, as many machine learning and deep learning techniques are helping to solve these contemporary difficulties. SVM gives more accuracy compared to other machine learning algorithms to predict the mental illness.

Details

1009240
Business indexing term
Title
AN EFFCIET FORCASTING MENTAL HEALTH CONDITION USING MACHINE LEARNING
Author
Babu, P Bujji 1 ; Jayalakshmi, Vishnumolakala 1 ; Harsha, Pagabala Naga Venkata Sri 1 ; Karthik, Palla 1 ; Sadhanala, Venkata Avinash 1 

 Department of CSE & AI, Chalapathi Institute of Engineering and Technology, LAM, Guntur, Andhra Pradesh, India 
Volume
15
Issue
1
Pages
146-150
Publication year
2024
Publication date
2024
Section
Research Article
Publisher
Ninety Nine Publication
Place of publication
Gurgaon
Country of publication
India
Publication subject
e-ISSN
13094653
Source type
Scholarly Journal
Language of publication
English
Document type
Journal Article
ProQuest document ID
3065454777
Document URL
https://www.proquest.com/scholarly-journals/effciet-forcasting-mental-health-condition-using/docview/3065454777/se-2?accountid=208611
Copyright
© 2024. This work is published under https://creativecommons.org/licenses/by/4.0 (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Last updated
2025-07-15
Database
ProQuest One Academic