Depression Detection Using Machine Learning: A Comprehensive Review

Year : 2025 | Volume : 12 | Issue : 03 | Page : 27 32
    By

    Sheshang Degadwala,

  • Shreyas Patel,

  • Malini Joshi,

  • Dharvi Soni,

  1. Professor & 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 remains one of the most prevalent mental health conditions globally, yet it frequently goes undiagnosed due to the reliance on subjective evaluation methods. With the growing availability of digital behavioral data and significant progress in machine learning (ML), new possibilities have emerged for the automated detection of depression. This review offers a detailed examination of recent advancements in ML-driven approaches to identifying depressive symptoms. It covers a range of topics, including the types of data sources used (such as social media, smartphone usage, and physiological signals), strategies for extracting relevant features, machine learning models employed, and the techniques used to evaluate their effectiveness. Furthermore, the review addresses the limitations and challenges currently faced in the field, such as data privacy concerns, generalizability, and model transparency. It also discusses promising directions for future research, emphasizing the importance of building ethical, explainable, and clinically relevant ML systems. The aim is to deliver a cohesive overview that not only synthesizes the current state of research but also offers guidance to future researchers and practitioners striving to develop responsible and accurate tools for mental health evaluation and support.

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):27-32.
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):27-32. Available from: https://journals.stmjournals.com/joadms/article=2025/view=229338


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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
219

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