Prevalence and predicting factors of perceived stress among Bangladeshi university students using machine learning algorithms.
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 ba...
| Published in: | Journal of Health, Population & Nutrition Vol. 40; no. 1; pp. 1 - 13 |
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| Main Authors: | , , , |
| Format: | Journal Article |
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BioMed Central
11/27/2021
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=153819254&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 153819254 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 16060997 HII jtl: Journal of Health, Population & Nutrition issn: 16060997 maglogo: N pubinfo: dt: 11/27/2021 vid: 40 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 153819254 153819254 NLM34838133 10.1186/s41043-021-00276-5 NLM34838133 153819254 ppf: 1 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Prevalence and predicting factors of perceived stress among Bangladeshi university students using machine learning algorithms. aug: au: Rois, Rumana Ray, Manik Rahman, Atikur Roy, Swapan K. affil: Department of Statistics, Jahangirnagar University, Dhaka, Bangladesh sug: subj: Colleges and Universities Stress, Psychological Epidemiology Students Prevalence Scales ab: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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