Abstract
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.
Publisher
Springer Science and Business Media LLC
Subject
Health, Toxicology and Mutagenesis,Public Health, Environmental and Occupational Health,Food Science
Reference68 articles.
1. Stress and our mental health: what is the impact & how can we tackle it? MQ Mental Health 2018. https://www.mqmentalhealth.org/stress-and-mental-health. Accessed May 16, 2018.
2. Seaward BL. Managing Stress: Principles and Strategies for Health and Well-Being (3rd ed.) 2002. Boston, MA: Jones and Bartlett Publishers.
3. Oswalt SB, Riddock CC. What to do about being overwhelmed: Graduate students, stress and university services. Coll Stud Aff J. 2007;27(1):24–44.
4. Saleem S, Mahmood Z. Mental health problems in university students: A prevalence study. FWU J Soc Sci. 2013;7(2):124–30.
5. Rodgers LS, Tennison LR. A preliminary assessment of adjustment disorder among FirstYear College Students. Arch Psychiatr Nurs. 2009;23(3):220–30.