Depression Identification Using Machine Learning Classifiers

Sakshi Srivastava; Ruchi Pandey; Shuvam Kumar Gupta; Saurabh Nayak

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.

Keywords

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

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Cites this article as

S. Srivastava, R. Pandey, S. K. Gupta, S. Nayak, "Depression Identification Using Machine Learning Classifiers", International Journal of Innovative Research in Computer Science and Technology (IJIRCST), Vol-11, no.6, pp.1-5, 2023. Available from: https://doi.org/10.55524/ijircst.2023.11.6.1

Corresponding Author

Sakshi Srivastava

Department of Information Technology, KIET Group of Institutions, Ghaziabad, India

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