ItemConference Paper

Unlocking Insights into Mental Well-Being: A Deep Learning Approach to Depression Detection

Title
Unlocking Insights into Mental Well-Being: A Deep Learning Approach to Depression Detection
Creator
Sambandam, Rakoth Kandan; Vetriveeran, Divya; Jenefa, J.; Vinodha, D.; Thaiyalnayaki, S.
Description
Depression, a prevalent global mental health disorder, poses significant challenges for timely diagnosis and effective intervention. Our research leverages the power of deep learning to develop a novel approach to depression detection, with the ultimate goal of enhancing early diagnosis and improving mental well-being. To evaluate the effectiveness of our method, we carried out an examination on a sizable dataset consisting of real-world data from individuals with and without diagnosed depression. The report discusses the performance metrics to examine the proposed technique effectiveness in depression detection. Additionally, this proposed work emphasizes the ethical and privacy considerations surrounding mental health data. The findings of this research indicate promising results in early depression detection, offering the potential to revolutionize mental health care by facilitating timely interventions. We discuss the implications of our approach in terms of supporting mental health professionals, improving mental health care accessibility, and addressing the pressing global mental health crisis. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Source
Lecture Notes in Networks and Systems;Volume;1200;pp.535-545
Date
01-01-2025
Publisher
Springer Science and Business Media Deutschland GmbH
Subject
Deep learning; Depression; Healthcare; Mental well-being; Optimization
Coverage
Sambandam R.K., Department of CSE, SOET, CHRIST (Deemed to be University), Kengeri Campus, Karnataka, Bengaluru, India; Vetriveeran D., Department of CSE, SOET, CHRIST (Deemed to be University), Kengeri Campus, Karnataka, Bengaluru, India; Jenefa J., Department of CSE, SOET, CHRIST (Deemed to be University), Kengeri Campus, Karnataka, Bengaluru, India; Vinodha D., Department of CSE, SOET, CHRIST (Deemed to be University), Kengeri Campus, Karnataka, Bengaluru, India; Thaiyalnayaki S., Department of CSE, School of Computing, Bharath Institute of Higher Education and Research (Deemed to be University), Tamil Nadu, Chennai, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISSN: 23673370; ISBN: 978-981979925-1;
Format
online
Language
English
Type
Conference paper

Citation

Sambandam, Rakoth Kandan; Vetriveeran, Divya; Jenefa, J.; Vinodha, D.; Thaiyalnayaki, S., “Unlocking Insights into Mental Well-Being: A Deep Learning Approach to Depression Detection,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 19, 2026, https://archives.christuniversity.in/items/show/25692.

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