Machine learning models predict the emergence of depression in Argentinean college students during periods of COVID-19 quarantine
IntroductionThe 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...
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| Format: | Article |
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Frontiers Media S.A.
2024-04-01
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| Series: | Frontiers in Psychiatry |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpsyt.2024.1376784/full |
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| author | Lorena Cecilia López Steinmetz Lorena Cecilia López Steinmetz Margarita Sison Rustam Zhumagambetov Juan Carlos Godoy Stefan Haufe Stefan Haufe Stefan Haufe Stefan Haufe |
| author_facet | Lorena Cecilia López Steinmetz Lorena Cecilia López Steinmetz Margarita Sison Rustam Zhumagambetov Juan Carlos Godoy Stefan Haufe Stefan Haufe Stefan Haufe Stefan Haufe |
| author_sort | Lorena Cecilia López Steinmetz |
| collection | DOAJ |
| description | IntroductionThe 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.Aims1) 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.MethodsA 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.ResultsThe 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.DiscussionThese 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. |
| first_indexed | 2024-04-24T08:54:10Z |
| format | Article |
| id | doaj.art-3d08726a04c1417ba78e056330d088e2 |
| institution | Directory Open Access Journal |
| issn | 1664-0640 |
| language | English |
| last_indexed | 2024-04-24T08:54:10Z |
| publishDate | 2024-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Psychiatry |
| spelling | doaj.art-3d08726a04c1417ba78e056330d088e22024-04-16T08:16:05ZengFrontiers Media S.A.Frontiers in Psychiatry1664-06402024-04-011510.3389/fpsyt.2024.13767841376784Machine learning models predict the emergence of depression in Argentinean college students during periods of COVID-19 quarantineLorena Cecilia López Steinmetz0Lorena Cecilia López Steinmetz1Margarita Sison2Rustam Zhumagambetov3Juan Carlos Godoy4Stefan Haufe5Stefan Haufe6Stefan Haufe7Stefan Haufe8Inverse Modeling and Machine Learning, Chair of Uncertainty, Institute of Software Engineering and Theoretical Computer Science, Faculty IV Electrical Engineering and Computer Science, Technische Universität Berlin, Berlin, GermanyInstituto de Investigaciones Psicológicas (IIPsi), Facultad de Psicología, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Universidad Nacional de Córdoba (UNC), Córdoba, ArgentinaBerlin Center for Advanced Neuroimaging (BCAN), Charité – Universitätsmedizin Berlin, Berlin, GermanyWorking Group 8.44 Machine Learning and Uncertainty, Mathematical Modelling and Data Analysis Department, Physikalisch-Technische Bundesanstalt Braunschweig und Berlin, Berlin, GermanyInstituto de Investigaciones Psicológicas (IIPsi), Facultad de Psicología, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Universidad Nacional de Córdoba (UNC), Córdoba, ArgentinaInverse Modeling and Machine Learning, Chair of Uncertainty, Institute of Software Engineering and Theoretical Computer Science, Faculty IV Electrical Engineering and Computer Science, Technische Universität Berlin, Berlin, GermanyBerlin Center for Advanced Neuroimaging (BCAN), Charité – Universitätsmedizin Berlin, Berlin, GermanyWorking Group 8.44 Machine Learning and Uncertainty, Mathematical Modelling and Data Analysis Department, Physikalisch-Technische Bundesanstalt Braunschweig und Berlin, Berlin, GermanyInstitute for Medical Informatics, Charité – Universitätsmedizin Berlin, Berlin, GermanyIntroductionThe 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.Aims1) 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.MethodsA 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.ResultsThe 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.DiscussionThese 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.https://www.frontiersin.org/articles/10.3389/fpsyt.2024.1376784/fulldepression predictionCOVID-19 pandemicmachine learningclassificationregressioncollege students |
| spellingShingle | Lorena Cecilia López Steinmetz Lorena Cecilia López Steinmetz Margarita Sison Rustam Zhumagambetov Juan Carlos Godoy Stefan Haufe Stefan Haufe Stefan Haufe Stefan Haufe Machine learning models predict the emergence of depression in Argentinean college students during periods of COVID-19 quarantine Frontiers in Psychiatry depression prediction COVID-19 pandemic machine learning classification regression college students |
| title | 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_short | 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 |
| topic | depression prediction COVID-19 pandemic machine learning classification regression college students |
| url | https://www.frontiersin.org/articles/10.3389/fpsyt.2024.1376784/full |
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