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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/234968
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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 |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/url/https://www.frontiersin.org/articles/10.3389/fpsyt.2024.1376784/full 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/ |
eu_rights_str_mv |
openAccess |
rights_invalid_str_mv |
https://creativecommons.org/licenses/by/2.5/ar/ |
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application/pdf application/pdf |
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Frontiers Media |
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Frontiers Media |
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reponame:CONICET Digital (CONICET) instname:Consejo Nacional de Investigaciones Científicas y Técnicas |
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CONICET Digital (CONICET) |
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Consejo Nacional de Investigaciones Científicas y Técnicas |
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CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas |
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dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar |
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