Comparison of the Performance of Machine Learning-based Algorithms for Predicting Depression and Anxiety among University Students in Bangladesh

Abstract

Introduction: The purpose of this research was to predict mental illness among university students using various machine learning (ML) algorithms. Methods: A structured questionnaire-based online survey was conducted on 2121 university students (private and public) living in Bangladesh. After obtaining informed consent, the participants completed a web-based survey examining sociodemographic variables and behavioral tests (including the Patient Health Questionnaire (PHQ-9) scale and the Generalized Anxiety Disorder Assessment-7 scale). This study applied six well-known ML algorithms, namely logistic regression, random forest (RF), support vector machine (SVM), linear discriminate analysis, K-nearest neighbors, Naïve Bayes, and which were used to predict mental illness among university students from Dhaka city in Bangladesh. Results: Of the 2121 eligible respondents, 45% were male and 55% were female, and approximately 76.9% were 21–25 years old. The prevalence of severe depression and severe anxiety was higher for women than for men. Based on various performance parameters, the results of the accuracy assessment showed that RF outperformed other models for the prediction of depression (89% accuracy), while SVM provided the best result than other models for the prediction of anxiety (91.49% accuracy). Conclusion: Based on these findings, we recommend that the RF algorithm and the SVM algorithm were more moderate than any other ML algorithm used in this study to predict the mental health status of university students in Bangladesh (depression and anxiety, respectively). Finally, this study proposes to apply RF and SVM classification when the prediction of mental illness status is the core interest.

How are Brazilian university students coping with the COVID-19 pandemic? Results of an online survey on psychosocial well-being, perceived burdens, and attitudes toward social distancing and vaccination

“…Surprisingly, differently from what was observed in other studies with university students and the general population [ 5 , 21 , 51 , 73 , 75 ], this sample did not show a significant statistical difference among female or diverse genders as predictor variables of depressive symptoms, and neither to not being a parent [ 5 , 8 , 21 , 27 , 73 ].…”

“…Collateral effects of social distancing and its outcomes have been broadly stated [11][12][13][14][15][16][17][18][19][20], and negative impacts such as feelings of despair, anxiety, confusion, anger, irritability, fear, frustration, and boredom have been reported among the general population [11][12][13][20][21][22][23][24]. Besides, increased perceived stress, sleep disorders, post-traumatic stress symptoms, and suicidal ideation have also been described as possible outcomes of the pandemic [11, 13,21,23,[25][26][27], and less perceived support has been associated with suicidal ideation [23].…”

Association between quarantine and sleep disturbance in Hong Kong adults: The mediating role of COVID-19 mental impact and distress

“…Coronavirus disease 2019 (COVID- 19) is a global pandemic that caused 6.6 million deaths since early 2020. The COVID-19 pandemic caused various mental health problems such as anxiety, depression, social stigma, and mental stress across different populations in general (1)(2)(3). Given its contagious nature and fatality (4), various policies such as border controls, contact tracing, social distancing, and restriction of community activities (5) were used to curb the spread rate of coronavirus worldwide.…”

Predictors of COVID‐19 vaccine hesitancy among Jordanian older adults

“…Fear of COVID‐19 appears to contribute to mental health problems, including depression, anxiety, stress, sleep problems and impaired mental well‐being (Sandya et al, 2022). According to Nayan et al (2022), females experienced higher rates of depression than males during the COVID‐19 pandemic. Furthermore, the rates of depression and anxiety among students were ~26.7% and 22%, respectively (Nayan et al, 2022).…”