J Health Popul Nutr. 2021 Nov 27;40(1):50. doi: 10.1186/s41043-021-00276-5.
Prevalence and predicting factors of perceived stress among Bangladeshi university students using machine learning algorithms.
Journal of health, population, and nutrition
Rumana Rois, Manik Ray, Atikur Rahman, Swapan K Roy
Affiliations
Affiliations
- Department of Statistics, Jahangirnagar University, Dhaka, Bangladesh. [email protected].
- Department of Statistics, Jahangirnagar University, Dhaka, Bangladesh.
- Bangladesh Breastfeeding Foundation (BBF), Institute of Public Health, Dhaka, Bangladesh.
PMID: 34838133
PMCID: PMC8627029 DOI: 10.1186/s41043-021-00276-5
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 (LR) model. As an alternative, we used the advanced machine learning (ML) approaches for detecting significant risk factors and to predict the prevalence of stress among Bangladeshi university students.
METHODS: This prevalence study surveyed 355 students from twenty-eight different Bangladeshi universities using questions concerning anthropometric measurements, academic, lifestyles, and health-related information, which referred to the perceived stress status of the respondents (yes or no). Boruta algorithm was used in determining the significant prognostic factors of the prevalence of stress. Prediction models were built using decision tree (DT), random forest (RF), support vector machine (SVM), and LR, and their performances were evaluated using parameters of confusion matrix, receiver operating characteristics (ROC) curves, and k-fold cross-validation techniques.
RESULTS: One-third of university students reported stress within the last 12 months. Students' pulse rate, systolic and diastolic blood pressures, sleep status, smoking status, and academic background were selected as the important features for predicting the prevalence of stress. Evaluated performance revealed that the highest performance observed from RF (accuracy = 0.8972, precision = 0.9241, sensitivity = 0.9250, specificity = 0.8148, area under the ROC curve (AUC) = 0.8715, k-fold accuracy = 0.8983) and the lowest from LR (accuracy = 0.7476, precision = 0.8354, sensitivity = 0.8250, specificity = 0.5185, AUC = 0.7822, k-fold accuracy = 07713) and SVM with polynomial kernel of degree 2 (accuracy = 0.7570, precision = 0.7975, sensitivity = 0.8630, specificity = 0.5294, AUC = 0.7717, k-fold accuracy = 0.7855). Overall, the RF model performs better and authentically predicted stress compared with other ML techniques, including individual and interaction effects of predictors.
CONCLUSION: The machine learning framework can be detected the significant prognostic factors and predicted this psychological problem more accurately, thereby helping the policy-makers, stakeholders, and families to understand and prevent this serious crisis by improving policy-making strategies, mental health promotion, and establishing effective university counseling services.
© 2021. The Author(s).
Keywords: Confusion matrix; Decision tree; Feature selection; Mental health; ROC; Random forest; Support vector machine; k-fold cross-validation
References
- Asian J Psychiatr. 2013 Aug;6(4):318-23 - PubMed
- J Affect Disord. 2018 Jan 1;225:97-103 - PubMed
- Gen Hosp Psychiatry. 2006 Mar-Apr;28(2):169-73 - PubMed
- PLoS One. 2020 Dec 31;15(12):e0245083 - PubMed
- Neuropsychiatr Dis Treat. 2015 Jul 16;11:1713-22 - PubMed
- Child Youth Serv Rev. 2020 Sep;116:105254 - PubMed
- Soc Psychiatry Psychiatr Epidemiol. 2008 Aug;43(8):667-72 - PubMed
- Int J Ment Health Addict. 2021 Jan 4;:1-16 - PubMed
- IEEE Trans Neural Netw. 2001;12(2):181-201 - PubMed
- J Relig Health. 2017 Aug;56(4):1170-1179 - PubMed
- Nat Med. 2019 Jan;25(1):44-56 - PubMed
- World Psychiatry. 2006 Feb;5(1):51-2 - PubMed
- Arch Psychiatr Nurs. 2009 Jun;23(3):220-30 - PubMed
- J Affect Disord. 2015 Mar 1;173:90-6 - PubMed
- J Family Community Med. 2012 May;19(2):105-12 - PubMed
- BMC Psychiatry. 2014 Jul 30;14:216 - PubMed
- J Am Coll Health. 1997 May;45(6):252-62 - PubMed
- Psychiatry Res. 2019 Jan;271:628-633 - PubMed
- Psychiatry J. 2017;2017:3047025 - PubMed
- JAMA. 2003 May 21;289(19):2560-72 - PubMed
- Am J Orthopsychiatry. 2007 Oct;77(4):534-42 - PubMed
- J Adolesc Health. 2010 Jan;46(1):3-10 - PubMed
- Asian J Psychiatr. 2019 Jan;39:84-85 - PubMed
- J Ment Health. 2018 Jun;27(3):193-196 - PubMed
- Soc Psychiatry Psychiatr Epidemiol. 2010 Feb;45(2):189-99 - PubMed
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