Machine learning models predict the emergence of depression in Argentinean college students during periods of COVID-19 quarantine

Autores
López Steinmetz, Lorena Cecilia; Sison, Margarita; Zhumagambetov, Rustam; Godoy, Juan Carlos; Haufe, Stefan
Año de publicación
2024
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Introduction: The COVID-19 pandemic has exacerbated mental health challenges, particularly depression among college students. Detecting at-risk students early is crucial but remains challenging, particularly in developing countries. Utilizing data-driven predictive models presents a viable solution to address this pressing need. Aims: 1) To develop and compare machine learning (ML) models for predicting depression in Argentinean students during the pandemic. 2) To assess the performance of classification and regression models using appropriate metrics. 3) To identify key features driving depression prediction. Methods: A longitudinal dataset (N = 1492 college students) captured T1 and T2 measurements during the Argentinean COVID-19 quarantine. ML models, including linear logistic regression classifiers/ridge regression (LogReg/RR), random forest classifiers/regressors, and support vector machines/regressors (SVM/SVR), are employed. Assessed features encompass depression and anxiety scores (at T1), mental disorder/suicidal behavior history, quarantine sub-period information, sex, and age. For classification, models’ performance on test data is evaluated using Area Under the Precision-Recall Curve (AUPRC), Area Under the Receiver Operating Characteristic curve, Balanced Accuracy, F1 score, and Brier loss. For regression, R-squared (R2), Mean Absolute Error, and Mean Squared Error are assessed. Univariate analyses are conducted to assess the predictive strength of each individual feature with respect to the target variable. The performance of multi- vs univariate models is compared using the mean AUPRC score for classifiers and the R2 score for regressors. Results: The highest performance is achieved by SVM and LogReg (e.g., AUPRC: 0.76, 95% CI: 0.69, 0.81) and SVR and RR models (e.g., R2 for SVR and RR: 0.56, 95% CI: 0.45, 0.64 and 0.45, 0.63, respectively). Univariate models, particularly LogReg and SVM using depression (AUPRC: 0.72, 95% CI: 0.64, 0.79) or anxiety scores (AUPRC: 0.71, 95% CI: 0.64, 0.78) and RR using depression scores (R2: 0.48, 95% CI: 0.39, 0.57) exhibit performance levels close to those of the multivariate models, which include all features. Discussion: These findings highlight the relevance of pre-existing depression and anxiety conditions in predicting depression during quarantine, underscoring their comorbidity. ML models, particularly SVM/SVR and LogReg/RR, demonstrate potential in the timely detection of at-risk students. However, further studies are needed before clinical implementation.
Fil: López Steinmetz, Lorena Cecilia. Universidad Nacional de Córdoba. Instituto de Investigaciones Psicológicas. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones Psicológicas; Argentina. Technishe Universitat Berlin; Alemania
Fil: Sison, Margarita. Charité Universitätsmedizin Berlin; Alemania
Fil: Zhumagambetov, Rustam. Physikalisch-technische Bundesanstalt; Alemania
Fil: Godoy, Juan Carlos. Universidad Nacional de Córdoba. Instituto de Investigaciones Psicológicas. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones Psicológicas; Argentina
Fil: Haufe, Stefan. Charité Universitätsmedizin Berlin; Alemania. Technishe Universitat Berlin; Alemania
Materia
Depression prediction
COVID-19
Machine learning
Classification
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/234968

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network_name_str CONICET Digital (CONICET)
spelling Machine learning models predict the emergence of depression in Argentinean college students during periods of COVID-19 quarantineLópez Steinmetz, Lorena CeciliaSison, MargaritaZhumagambetov, RustamGodoy, Juan CarlosHaufe, StefanDepression predictionCOVID-19Machine learningClassificationhttps://purl.org/becyt/ford/5.1https://purl.org/becyt/ford/5Introduction: The COVID-19 pandemic has exacerbated mental health challenges, particularly depression among college students. Detecting at-risk students early is crucial but remains challenging, particularly in developing countries. Utilizing data-driven predictive models presents a viable solution to address this pressing need. Aims: 1) To develop and compare machine learning (ML) models for predicting depression in Argentinean students during the pandemic. 2) To assess the performance of classification and regression models using appropriate metrics. 3) To identify key features driving depression prediction. Methods: A longitudinal dataset (N = 1492 college students) captured T1 and T2 measurements during the Argentinean COVID-19 quarantine. ML models, including linear logistic regression classifiers/ridge regression (LogReg/RR), random forest classifiers/regressors, and support vector machines/regressors (SVM/SVR), are employed. Assessed features encompass depression and anxiety scores (at T1), mental disorder/suicidal behavior history, quarantine sub-period information, sex, and age. For classification, models’ performance on test data is evaluated using Area Under the Precision-Recall Curve (AUPRC), Area Under the Receiver Operating Characteristic curve, Balanced Accuracy, F1 score, and Brier loss. For regression, R-squared (R2), Mean Absolute Error, and Mean Squared Error are assessed. Univariate analyses are conducted to assess the predictive strength of each individual feature with respect to the target variable. The performance of multi- vs univariate models is compared using the mean AUPRC score for classifiers and the R2 score for regressors. Results: The highest performance is achieved by SVM and LogReg (e.g., AUPRC: 0.76, 95% CI: 0.69, 0.81) and SVR and RR models (e.g., R2 for SVR and RR: 0.56, 95% CI: 0.45, 0.64 and 0.45, 0.63, respectively). Univariate models, particularly LogReg and SVM using depression (AUPRC: 0.72, 95% CI: 0.64, 0.79) or anxiety scores (AUPRC: 0.71, 95% CI: 0.64, 0.78) and RR using depression scores (R2: 0.48, 95% CI: 0.39, 0.57) exhibit performance levels close to those of the multivariate models, which include all features. Discussion: These findings highlight the relevance of pre-existing depression and anxiety conditions in predicting depression during quarantine, underscoring their comorbidity. ML models, particularly SVM/SVR and LogReg/RR, demonstrate potential in the timely detection of at-risk students. However, further studies are needed before clinical implementation.Fil: López Steinmetz, Lorena Cecilia. Universidad Nacional de Córdoba. Instituto de Investigaciones Psicológicas. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones Psicológicas; Argentina. Technishe Universitat Berlin; AlemaniaFil: Sison, Margarita. Charité Universitätsmedizin Berlin; AlemaniaFil: Zhumagambetov, Rustam. Physikalisch-technische Bundesanstalt; AlemaniaFil: Godoy, Juan Carlos. Universidad Nacional de Córdoba. Instituto de Investigaciones Psicológicas. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones Psicológicas; ArgentinaFil: Haufe, Stefan. Charité Universitätsmedizin Berlin; Alemania. Technishe Universitat Berlin; AlemaniaFrontiers Media2024-04-16info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/234968López Steinmetz, Lorena Cecilia; Sison, Margarita; Zhumagambetov, Rustam; Godoy, Juan Carlos; Haufe, Stefan; Machine learning models predict the emergence of depression in Argentinean college students during periods of COVID-19 quarantine; Frontiers Media; Frontiers in Psychiatry; 15; 1376784; 16-4-2024; 1-151664-0640CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.frontiersin.org/articles/10.3389/fpsyt.2024.1376784/fullinfo:eu-repo/semantics/altIdentifier/doi/10.3389/fpsyt.2024.1376784info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:34:22Zoai:ri.conicet.gov.ar:11336/234968instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:34:22.566CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Machine learning models predict the emergence of depression in Argentinean college students during periods of COVID-19 quarantine
title Machine learning models predict the emergence of depression in Argentinean college students during periods of COVID-19 quarantine
spellingShingle Machine learning models predict the emergence of depression in Argentinean college students during periods of COVID-19 quarantine
López Steinmetz, Lorena Cecilia
Depression prediction
COVID-19
Machine learning
Classification
title_short Machine learning models predict the emergence of depression in Argentinean college students during periods of COVID-19 quarantine
title_full Machine learning models predict the emergence of depression in Argentinean college students during periods of COVID-19 quarantine
title_fullStr Machine learning models predict the emergence of depression in Argentinean college students during periods of COVID-19 quarantine
title_full_unstemmed Machine learning models predict the emergence of depression in Argentinean college students during periods of COVID-19 quarantine
title_sort Machine learning models predict the emergence of depression in Argentinean college students during periods of COVID-19 quarantine
dc.creator.none.fl_str_mv López Steinmetz, Lorena Cecilia
Sison, Margarita
Zhumagambetov, Rustam
Godoy, Juan Carlos
Haufe, Stefan
author López Steinmetz, Lorena Cecilia
author_facet López Steinmetz, Lorena Cecilia
Sison, Margarita
Zhumagambetov, Rustam
Godoy, Juan Carlos
Haufe, Stefan
author_role author
author2 Sison, Margarita
Zhumagambetov, Rustam
Godoy, Juan Carlos
Haufe, Stefan
author2_role author
author
author
author
dc.subject.none.fl_str_mv Depression prediction
COVID-19
Machine learning
Classification
topic Depression prediction
COVID-19
Machine learning
Classification
purl_subject.fl_str_mv https://purl.org/becyt/ford/5.1
https://purl.org/becyt/ford/5
dc.description.none.fl_txt_mv Introduction: The COVID-19 pandemic has exacerbated mental health challenges, particularly depression among college students. Detecting at-risk students early is crucial but remains challenging, particularly in developing countries. Utilizing data-driven predictive models presents a viable solution to address this pressing need. Aims: 1) To develop and compare machine learning (ML) models for predicting depression in Argentinean students during the pandemic. 2) To assess the performance of classification and regression models using appropriate metrics. 3) To identify key features driving depression prediction. Methods: A longitudinal dataset (N = 1492 college students) captured T1 and T2 measurements during the Argentinean COVID-19 quarantine. ML models, including linear logistic regression classifiers/ridge regression (LogReg/RR), random forest classifiers/regressors, and support vector machines/regressors (SVM/SVR), are employed. Assessed features encompass depression and anxiety scores (at T1), mental disorder/suicidal behavior history, quarantine sub-period information, sex, and age. For classification, models’ performance on test data is evaluated using Area Under the Precision-Recall Curve (AUPRC), Area Under the Receiver Operating Characteristic curve, Balanced Accuracy, F1 score, and Brier loss. For regression, R-squared (R2), Mean Absolute Error, and Mean Squared Error are assessed. Univariate analyses are conducted to assess the predictive strength of each individual feature with respect to the target variable. The performance of multi- vs univariate models is compared using the mean AUPRC score for classifiers and the R2 score for regressors. Results: The highest performance is achieved by SVM and LogReg (e.g., AUPRC: 0.76, 95% CI: 0.69, 0.81) and SVR and RR models (e.g., R2 for SVR and RR: 0.56, 95% CI: 0.45, 0.64 and 0.45, 0.63, respectively). Univariate models, particularly LogReg and SVM using depression (AUPRC: 0.72, 95% CI: 0.64, 0.79) or anxiety scores (AUPRC: 0.71, 95% CI: 0.64, 0.78) and RR using depression scores (R2: 0.48, 95% CI: 0.39, 0.57) exhibit performance levels close to those of the multivariate models, which include all features. Discussion: These findings highlight the relevance of pre-existing depression and anxiety conditions in predicting depression during quarantine, underscoring their comorbidity. ML models, particularly SVM/SVR and LogReg/RR, demonstrate potential in the timely detection of at-risk students. However, further studies are needed before clinical implementation.
Fil: López Steinmetz, Lorena Cecilia. Universidad Nacional de Córdoba. Instituto de Investigaciones Psicológicas. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones Psicológicas; Argentina. Technishe Universitat Berlin; Alemania
Fil: Sison, Margarita. Charité Universitätsmedizin Berlin; Alemania
Fil: Zhumagambetov, Rustam. Physikalisch-technische Bundesanstalt; Alemania
Fil: Godoy, Juan Carlos. Universidad Nacional de Córdoba. Instituto de Investigaciones Psicológicas. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones Psicológicas; Argentina
Fil: Haufe, Stefan. Charité Universitätsmedizin Berlin; Alemania. Technishe Universitat Berlin; Alemania
description Introduction: The COVID-19 pandemic has exacerbated mental health challenges, particularly depression among college students. Detecting at-risk students early is crucial but remains challenging, particularly in developing countries. Utilizing data-driven predictive models presents a viable solution to address this pressing need. Aims: 1) To develop and compare machine learning (ML) models for predicting depression in Argentinean students during the pandemic. 2) To assess the performance of classification and regression models using appropriate metrics. 3) To identify key features driving depression prediction. Methods: A longitudinal dataset (N = 1492 college students) captured T1 and T2 measurements during the Argentinean COVID-19 quarantine. ML models, including linear logistic regression classifiers/ridge regression (LogReg/RR), random forest classifiers/regressors, and support vector machines/regressors (SVM/SVR), are employed. Assessed features encompass depression and anxiety scores (at T1), mental disorder/suicidal behavior history, quarantine sub-period information, sex, and age. For classification, models’ performance on test data is evaluated using Area Under the Precision-Recall Curve (AUPRC), Area Under the Receiver Operating Characteristic curve, Balanced Accuracy, F1 score, and Brier loss. For regression, R-squared (R2), Mean Absolute Error, and Mean Squared Error are assessed. Univariate analyses are conducted to assess the predictive strength of each individual feature with respect to the target variable. The performance of multi- vs univariate models is compared using the mean AUPRC score for classifiers and the R2 score for regressors. Results: The highest performance is achieved by SVM and LogReg (e.g., AUPRC: 0.76, 95% CI: 0.69, 0.81) and SVR and RR models (e.g., R2 for SVR and RR: 0.56, 95% CI: 0.45, 0.64 and 0.45, 0.63, respectively). Univariate models, particularly LogReg and SVM using depression (AUPRC: 0.72, 95% CI: 0.64, 0.79) or anxiety scores (AUPRC: 0.71, 95% CI: 0.64, 0.78) and RR using depression scores (R2: 0.48, 95% CI: 0.39, 0.57) exhibit performance levels close to those of the multivariate models, which include all features. Discussion: These findings highlight the relevance of pre-existing depression and anxiety conditions in predicting depression during quarantine, underscoring their comorbidity. ML models, particularly SVM/SVR and LogReg/RR, demonstrate potential in the timely detection of at-risk students. However, further studies are needed before clinical implementation.
publishDate 2024
dc.date.none.fl_str_mv 2024-04-16
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/234968
López Steinmetz, Lorena Cecilia; Sison, Margarita; Zhumagambetov, Rustam; Godoy, Juan Carlos; Haufe, Stefan; Machine learning models predict the emergence of depression in Argentinean college students during periods of COVID-19 quarantine; Frontiers Media; Frontiers in Psychiatry; 15; 1376784; 16-4-2024; 1-15
1664-0640
CONICET Digital
CONICET
url http://hdl.handle.net/11336/234968
identifier_str_mv López Steinmetz, Lorena Cecilia; Sison, Margarita; Zhumagambetov, Rustam; Godoy, Juan Carlos; Haufe, Stefan; Machine learning models predict the emergence of depression in Argentinean college students during periods of COVID-19 quarantine; Frontiers Media; Frontiers in Psychiatry; 15; 1376784; 16-4-2024; 1-15
1664-0640
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
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info:eu-repo/semantics/altIdentifier/doi/10.3389/fpsyt.2024.1376784
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
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dc.publisher.none.fl_str_mv Frontiers Media
publisher.none.fl_str_mv Frontiers Media
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
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repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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