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A Machine Learning Method for Detecting Depression Among College Students

by Peter J. Yu
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 185 - Number 24
Year of Publication: 2023
Authors: Peter J. Yu
10.5120/ijca2023923003

Peter J. Yu . A Machine Learning Method for Detecting Depression Among College Students. International Journal of Computer Applications. 185, 24 ( Jul 2023), 44-51. DOI=10.5120/ijca2023923003

@article{ 10.5120/ijca2023923003,
author = { Peter J. Yu },
title = { A Machine Learning Method for Detecting Depression Among College Students },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2023 },
volume = { 185 },
number = { 24 },
month = { Jul },
year = { 2023 },
issn = { 0975-8887 },
pages = { 44-51 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume185/number24/32844-2023923003/ },
doi = { 10.5120/ijca2023923003 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:26:59.969095+05:30
%A Peter J. Yu
%T A Machine Learning Method for Detecting Depression Among College Students
%J International Journal of Computer Applications
%@ 0975-8887
%V 185
%N 24
%P 44-51
%D 2023
%I Foundation of Computer Science (FCS), NY, USA
Abstract

As depression is becoming more prevalent on college campuses, it is increasingly a critical topic to investigate. Recently, studies using machine learning techniques have begun to predict depression and other mental illnesses. However, there is little understanding of why these mental problems occur. In this study, the causation of depression among college students posting on the popular social media platform Reddit is studied, and several machine learning classifiers for depression detection are compared. Of the 7,680 semi-anonymous Reddit posts examined, 552 contained depression-related keywords. After applying a series of natural language processing (NLP) techniques, three primary areas of depression were found among college students: institutions and programs; academic projects and assignments; and the college environment. Moreover, the results of this study show the effectiveness and performance of different machine learning classifiers. The classifier with the highest accuracy was Adaptive Boosting (AdaBoost), detecting depression with 99% accuracy, while the Random Forest classifier had the highest F1 score of 1.0.

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Index Terms

Computer Science
Information Sciences

Keywords

College College Students Depression Mental Health Machine Learning Natural Language Processing (NLP) Latent Dirichlet Allocation (LDA) Social Media Reddit

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