Prevalence and predicting factors of perceived stress among Bangladeshi university students using machine learning algorithms

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 usin...

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Autores principales: Rumana Rois, Manik Ray, Atikur Rahman, Swapan K. Roy
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Publicado: BMC 2021
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spelling oai:doaj.org-article:eeff821e883740ef97cc5f5782fc4def2021-11-28T12:08:06ZPrevalence and predicting factors of perceived stress among Bangladeshi university students using machine learning algorithms10.1186/s41043-021-00276-52072-1315https://doaj.org/article/eeff821e883740ef97cc5f5782fc4def2021-11-01T00:00:00Zhttps://doi.org/10.1186/s41043-021-00276-5https://doaj.org/toc/2072-1315Abstract 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.Rumana RoisManik RayAtikur RahmanSwapan K. RoyBMCarticleMental healthDecision treeRandom forestSupport vector machineFeature selectionConfusion matrixNutritional diseases. Deficiency diseasesRC620-627Public aspects of medicineRA1-1270ENJournal of Health, Population and Nutrition, Vol 40, Iss 1, Pp 1-12 (2021)
institution DOAJ
collection DOAJ
language EN
topic Mental health
Decision tree
Random forest
Support vector machine
Feature selection
Confusion matrix
Nutritional diseases. Deficiency diseases
RC620-627
Public aspects of medicine
RA1-1270
spellingShingle Mental health
Decision tree
Random forest
Support vector machine
Feature selection
Confusion matrix
Nutritional diseases. Deficiency diseases
RC620-627
Public aspects of medicine
RA1-1270
Rumana Rois
Manik Ray
Atikur Rahman
Swapan K. Roy
Prevalence and predicting factors of perceived stress among Bangladeshi university students using machine learning algorithms
description 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.
format article
author Rumana Rois
Manik Ray
Atikur Rahman
Swapan K. Roy
author_facet Rumana Rois
Manik Ray
Atikur Rahman
Swapan K. Roy
author_sort Rumana Rois
title Prevalence and predicting factors of perceived stress among Bangladeshi university students using machine learning algorithms
title_short Prevalence and predicting factors of perceived stress among Bangladeshi university students using machine learning algorithms
title_full Prevalence and predicting factors of perceived stress among Bangladeshi university students using machine learning algorithms
title_fullStr Prevalence and predicting factors of perceived stress among Bangladeshi university students using machine learning algorithms
title_full_unstemmed Prevalence and predicting factors of perceived stress among Bangladeshi university students using machine learning algorithms
title_sort prevalence and predicting factors of perceived stress among bangladeshi university students using machine learning algorithms
publisher BMC
publishDate 2021
url https://doaj.org/article/eeff821e883740ef97cc5f5782fc4def
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AT manikray prevalenceandpredictingfactorsofperceivedstressamongbangladeshiuniversitystudentsusingmachinelearningalgorithms
AT atikurrahman prevalenceandpredictingfactorsofperceivedstressamongbangladeshiuniversitystudentsusingmachinelearningalgorithms
AT swapankroy prevalenceandpredictingfactorsofperceivedstressamongbangladeshiuniversitystudentsusingmachinelearningalgorithms
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