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

Year : 2024 | Volume :15 | Issue : 03 | Page : 31-40
By
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Aastha Sharma,

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Shilpi Saxena,

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Jolly Pandey,

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Ashish Singh,

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Vaibhav Chaudhari,

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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

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Depression is basically a very common mental health disorder which results in disorder of person’s behaviour, emotions and cognitive abilities. The depression can be caused by the environmental factors or the hereditary factors. The person suffering from depression might have symptoms of suicidal thoughts, alter food patterns as well as sleeping issues.  Depression is a global issue which has impacted millions of people globally having more effect on women worldwide. The complexity of depression has led to the increased fascination towards search of treatments of depression as traditional methods lacks in accessing intensity and advancement of depression. This survey paper reviews the evolution in using Al and ML to estimate depression intensity across various context such as social media, clinical data and physical activity. These models generated using AI analyses the big data generated by person’s social media platform 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. Personalised health assessment can be done using AI to analyse one’s social media platform. The posts having text, audio, videos can reveal the information about a person’s mental condition. The thorough potential of AI and traditional methods in prediction depression intensity is highlighted in this paper along with the need for future research to improve the prediction accuracy, multimodal data fusion and big data. AI have 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 (joedt)]

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):31-40.
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):31-40. Available from: https://journals.stmjournals.com/joedt/article=2024/view=0

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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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