Depression Detection using Machine Learning: A Comprehensive Review

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This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2025 | Volume : 12 | Issue : 03 | Page :
    By

    Sheshang Degadwala,

  • Shreyas Patel,

  • Malini Joshi,

  • Dharvi Soni,

  1. Professor and Head, Department of Computer Engineering, Sigma University, Vadodara, Gujarat, India
  2. Lecturer, Department of Computer Engineering, Sigma University, Vadodara, Gujarat, India
  3. Assistant Professor, Department of Computer Engineering, Sigma University, Vadodara, Gujarat, India
  4. Assistant Professor, Department of Computer Engineering, Sigma University, Vadodara, Gujarat, India

Abstract

Depression is a leading mental health disorder worldwide, often underdiagnosed due to subjective assessment methods. The increasing availability of digital behavioral data and the advancement in machine learning (ML) have opened new avenues for automated depression detection. This review presents a comprehensive overview of recent developments in ML- based approaches for detecting depression. It explores data sources, feature extraction techniques, learning algorithms, evaluation methods, and highlights current challenges and future directions. The paper aims to provide an integrated understanding of the landscape and guide researchers in developing effective, ethical, and interpretable solutions for mental health assessment.

Keywords: Depression Detection, Machine Learning, Mental Health, Natural Language Processing, Multimodal Analysis, Explainable AI

[This article belongs to Journal of Advanced Database Management & Systems ]

How to cite this article:
Sheshang Degadwala, Shreyas Patel, Malini Joshi, Dharvi Soni. Depression Detection using Machine Learning: A Comprehensive Review. Journal of Advanced Database Management & Systems. 2025; 12(03):-.
How to cite this URL:
Sheshang Degadwala, Shreyas Patel, Malini Joshi, Dharvi Soni. Depression Detection using Machine Learning: A Comprehensive Review. Journal of Advanced Database Management & Systems. 2025; 12(03):-. Available from: https://journals.stmjournals.com/joadms/article=2025/view=223080


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Regular Issue Subscription Review Article
Volume 12
Issue 03
Received 24/07/2025
Accepted 06/08/2025
Published 11/08/2025
Publication Time 18 Days
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