Prevalence and predicting factors of perceived stress among Bangladeshi university students using machine learning algorithms Rumana Rois (
[email protected] ) Jahangirnagar University https://orcid.org/0000-0002-0751-7104 Manik Ray Jahangirnagar University Department of Statistics Atikur Rahman Jahangirnagar University Department of Statistics Swapan. K. Roy Bangladesh Breastfeeding Foundation Research article Keywords: Mental health, decision tree, random forest, support vector machine, feature selection, confusion matrix, ROC curves, k-fold cross-validation Posted Date: April 28th, 2021 DOI: https://doi.org/10.21203/rs.3.rs-468708/v1 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License Original research article Prevalence and predicting factors of perceived stress among Bangladeshi university students using machine learning algorithms Rumana Rois 1*, Manik Ray 2, Atikur Rahman 3, and Swapan. K. Roy 4 1 Department of Statistics, Jahangirnagar University, Dhaka, Bangladesh *Corresponding author’s Email:
[email protected] 2 Department of Statistics, Jahangirnagar University, Dhaka, Bangladesh Email:
[email protected] 3 Department of Statistics, Jahangirnagar University, Dhaka, Bangladesh Email:
[email protected] 4 Bangladesh Breastfeeding Foundation (BBF), Institute of Public Health, Dhaka, Bangladesh Email:
[email protected] 1 Abstract Background: Stress-related mental health problems are one of the most common causes of the burden in university students worldwide. Many studies have been conducted to predict the prevalence of stress among university students, however most of these analyses were predominantly performed using the basic logistic regression model. As an alternative, we used the advanced machine learning approaches for detecting significant risk factors and to predict the prevalence of stress among Bangladeshi university students.