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
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	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
Acceso en línea:	https://doaj.org/article/eeff821e883740ef97cc5f5782fc4def
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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
work_keys_str_mv	AT rumanarois prevalenceandpredictingfactorsofperceivedstressamongbangladeshiuniversitystudentsusingmachinelearningalgorithms AT manikray prevalenceandpredictingfactorsofperceivedstressamongbangladeshiuniversitystudentsusingmachinelearningalgorithms AT atikurrahman prevalenceandpredictingfactorsofperceivedstressamongbangladeshiuniversitystudentsusingmachinelearningalgorithms AT swapankroy prevalenceandpredictingfactorsofperceivedstressamongbangladeshiuniversitystudentsusingmachinelearningalgorithms
_version_	1718408232850948096

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

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