AI and Machine Learning Approaches for Estimating Depression Severity: Techniques, Trends, and Applications

Year : 2024 | Volume : 15 | Issue : 03 | Page : 29 38
    By

    Aastha Sharma,

  • Shilpi Saxena,

  • Jolly Pandey,

  • Ashish Singh,

  • Vaibhav Chaudhari,

  • Mritunjay Kr. Ranjan,

  1. Student, Department of Computer Application & Information Technology, Lords University, Alwar, Rajasthan, India
  2. Assistant Professor, Department of Computer Application & Information Technology, Lords University, Alwar, Rajasthan, India
  3. Assistant Professor, Department of Information Technology, Gaya College Gaya, Bihar, India
  4. Student, School of Computer Sciences and Engineering, Sandip University Nashik, Maharashtra, India
  5. Assistant Professor, School of Computer Sciences and Engineering, Sandip University Nashik, Maharashtra, India
  6. Assistant Professor, School of Computer Sciences and Engineering, Sandip University Nashik, Maharashtra, India

Abstract

Depression is a very common mental health disorder that results in a disorder of a person’s behavior, emotions, and cognitive abilities. Depression can be caused by environmental factors or hereditary factors. The person suffering from depression might have symptoms of suicidal thoughts, altering food patterns as well as sleeping issues. Depression is a global issue that has impacted millions of people globally having more effect on women worldwide. The complexity of depression has led to an increased fascination towards searching for treatments for depression as traditional methods lack in accessing intensity and advancement of depression. This survey paper reviews the evolution of using Al and machine learning (ML) to estimate depression intensity across various contexts such as social media, clinical data, and physical activity. These models generated using artificial intelligence (AI) analyze the big data generated by people’s social media platforms and clinical records which can predict the intensity of depression. Studies using deep learning models, multi-task architectures, and symptom-specific detection methods demonstrate the potential of AI in enhancing the accuracy of depression diagnosis. Personalized health assessment can be done using AI to analyze one’s social media platform. The posts having text, audio, and videos can reveal information about a person’s mental condition. The thorough potential of AI and traditional methods in the prediction of depression intensity is highlighted in this paper along with the need for future research to improve prediction accuracy, multimodal data fusion, and big data. AI has the potential to completely transform mental health.

Keywords: Depression, depression intensity, machine learning, big data, Electroconvulsive therapy (ECT).

[This article belongs to Journal of Electronic Design Technology ]

How to cite this article:
Aastha Sharma, Shilpi Saxena, Jolly Pandey, Ashish Singh, Vaibhav Chaudhari, Mritunjay Kr. Ranjan. AI and Machine Learning Approaches for Estimating Depression Severity: Techniques, Trends, and Applications. Journal of Electronic Design Technology. 2024; 15(03):29-38.
How to cite this URL:
Aastha Sharma, Shilpi Saxena, Jolly Pandey, Ashish Singh, Vaibhav Chaudhari, Mritunjay Kr. Ranjan. AI and Machine Learning Approaches for Estimating Depression Severity: Techniques, Trends, and Applications. Journal of Electronic Design Technology. 2024; 15(03):29-38. Available from: https://journals.stmjournals.com/joedt/article=2024/view=184059


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Regular Issue Subscription Original Research
Volume 15
Issue 03
Received 29/10/2024
Accepted 07/11/2024
Published 19/11/2024
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