Depression Identification Using Machine Learning Classifiers

Authors

  • Sakshi Srivastava B.Tech Scholar, Department of Information Technology, KIET Group of Institutions, Ghaziabad, India Author
  • Ruchi Pandey B.Tech Scholar, Department of Information Technology, KIET Group of Institutions, Ghaziabad, India Author
  • Shuvam Kumar Gupta B.Tech Scholar, Department of Information Technology, KIET Group of Institutions, Ghaziabad, India Author
  • Saurabh Nayak B.Tech Scholar, Department of Information Technology, KIET Group of Institutions, Ghaziabad, India Author
  • Manoj Kumar Senior Assistant Professor, Department of Computer Application, International Institute for Special Education (IISE), Lucknow, India Author

Keywords:

Depression, K nearest Neighbour, Support Vector Machine, Naive Bayes, Logistic Regression, Random Forest

Abstract

Depression is a mental condition that  indicates emotional issues, including anger issues,  unhappiness, boredom, appetite loss, lack of  concentration, anxiety, etc. The quality of life of an  individual may be negatively impacted by depression,  which may ultimately lead to loss of health and life. According to the World Health Organization, there are  300 million depressed persons worldwide in 2022. The  number of depression cases rose throughout the  pandemic. It became important to detect depression in  people accurately. During the construction of the model  various machine learning techniques were applied.  Support Vector Machine (SVM), Random Forest, Naive  Bayes, K Nearest Neighbour (KNN), and Logistic  Regression were used to test the accuracy of the model.  Among all techniques, Logistic Regression had the  highest accuracy. The proposed technique improved the  accuracy of 0.79 in comparison with the other existing  state of art. Physical health and mental health, both are  equally important. Early detection of depression is  necessary so that it can be treated in its early stage. 

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Published

2023-11-30

Issue

Vol. 11 No. 6 (2023): International Journal of Innovative Research in Computer Science and Technology (Nov) 2023

Section

Articles

How to Cite

Depression Identification Using Machine Learning Classifiers. (2023). International Journal of Innovative Research in Computer Science & Technology, 11(6), 1–5. Retrieved from https://www.acspublisher.com/journals/index.php/ijircst/article/view/11581