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dc.contributor.author
López Steinmetz, Lorena Cecilia
dc.contributor.author
Sison, Margarita
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Zhumagambetov, Rustam
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Godoy, Juan Carlos
dc.contributor.author
Haufe, Stefan
dc.date.available
2024-05-08T17:44:20Z
dc.date.issued
2024-04-16
dc.identifier.citation
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
dc.identifier.issn
1664-0640
dc.identifier.uri
http://hdl.handle.net/11336/234968
dc.description.abstract
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.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Frontiers Media
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by/2.5/ar/
dc.subject
Depression prediction
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COVID-19
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Machine learning
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Classification
dc.subject.classification
Psicología
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Psicología
dc.subject.classification
CIENCIAS SOCIALES
dc.title
Machine learning models predict the emergence of depression in Argentinean college students during periods of COVID-19 quarantine
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2024-05-07T13:07:17Z
dc.journal.volume
15
dc.journal.number
1376784
dc.journal.pagination
1-15
dc.journal.pais
Suiza
dc.journal.ciudad
Lausana
dc.description.fil
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
dc.description.fil
Fil: Sison, Margarita. Charité Universitätsmedizin Berlin; Alemania
dc.description.fil
Fil: Zhumagambetov, Rustam. Physikalisch-technische Bundesanstalt; Alemania
dc.description.fil
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
dc.description.fil
Fil: Haufe, Stefan. Charité Universitätsmedizin Berlin; Alemania. Technishe Universitat Berlin; Alemania
dc.journal.title
Frontiers in Psychiatry
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.frontiersin.org/articles/10.3389/fpsyt.2024.1376784/full
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.3389/fpsyt.2024.1376784
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