Detection of Depression Using Machine Learning Algorithms

Authors

  • M Ravi Kumar Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation
  • Kadoori Pooja Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation
  • Meghana Udathu Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation
  • Lakshmi Prasanna J Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation
  • Chella Santhosh Associate Professor, Department of ECE Koneru Lakshmaiah Education Foundation Vaddeswaram, Guntur (Dist), Andhra Pradesh, India - 522502.

DOI:

https://doi.org/10.3991/ijoe.v18i04.29051

Keywords:

Natural Language Processing Naives Bayes Logistic Regression

Abstract


Online media outlets such as Facebook, Twitter, and Instagram have forever altered our reality. People are now more connected than ever before, and they have developed such a sophisticated identity. According to ongoing research, there is a link between excessive usage of social media and depression. A mood illness is known as depression. It's defined as sadness, loss, or anger that interferes with a person's day-to-day activity. For different people, depression expresses itself in a number of ways. It might cause disturbances in your daily routine, resulting in missed time and lower productivity. It can also affect relationships as well as some chronic conditions. It has evolved into a serious disease in our generation, with the number of those affected increasing by the day. Some people, on the other hand, can confess that they are depressed, while others are utterly ignorant. On the other hand, the great majority Social media has evolved into a "diary," allowing them to share their mental condition.

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Published

2022-03-22

How to Cite

Ravi Kumar, M., Pooja, K. ., Udathu, M. ., J, L. P., & Santhosh, C. (2022). Detection of Depression Using Machine Learning Algorithms . International Journal of Online and Biomedical Engineering (iJOE), 18(04), pp. 155–163. https://doi.org/10.3991/ijoe.v18i04.29051

Issue

Section

Short Papers