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...
Guardado en:
Autores principales: | , , , |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
BMC
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/eeff821e883740ef97cc5f5782fc4def |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
id |
oai:doaj.org-article:eeff821e883740ef97cc5f5782fc4def |
---|---|
record_format |
dspace |
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 |