Detection of Depression using Various Machine Learning and Deep Learning Techniques: A Review

DOI

https://www.doi.org/10.59256/ijire.20260702005

Published In

Volume 7, Issue 2 (2026)

Published Online

March-April 2026

Pages

38-46

Article Type

Review Article

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Thoughts of suicide are sometimes prompted by depression and other mental illnesses, which continues to be a major issue in society. Scientists have been working on algorithms that can accurately detect depression in its early stages. Numerous investigations have previously been suggested. This study examines the research conducted to date on the use of approaches for the early diagnosis of depression by analyzing several previous studies based on deep learning and artificial intelligence (AI). Additional methods of identifying emotions are also covered, including the analysis of words on social media platforms, emotional chatbots, and facial expressions. Various techniques are utilized to recognize emotions for the purpose of detecting depression, such as Naive-Bayes, Support Vector Machines (SVM), Logistic Regression, etc. The goal of this paper is to examine different methods that aid in the early diagnosis of depression and the associated research questions