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
• Materia: Depression prediction; COVID-19 pandemic; Machine learning; Classification; Regression; College students; Longitudinal survey; Argentina
• Institución: Universidad Nacional de Córdoba

Fil: López Steinmetz, Lorena Cecilia. Universidad Nacional de Córdoba. Facultad de Psicología; Argentina.
Fil: López Steinmetz, Lorena Cecilia. Consejo Nacional de Investigaciones Científicas y Tecnológicas. Instituto de Investigaciones Psicológicas; Argentina.
Fil: López Steinmetz, Lorena Cecilia. Technische Universität Berlin. Faculty IV Electrical Engineering and Computer Science. Chair of Uncertainty. Institute of Software Engineering and Theoretical Computer Science. Inverse Modeling and Machine Learning; Germany.
Fil: Sison, Margarita. Charité – Universitätsmedizin Berlin. Berlin Center for Advanced Neuroimaging; Germany.
Fil: Zhumagambetov, Rustam. Working Group 8.44 Machine Learning and Uncertainty. Mathematical Modelling and Data Analysis Department. Physikalisch-Technische Bundesanstalt Braunschweig und Berlin; Germany.
Fil: Godoy, Juan Carlos. Universidad Nacional de Córdoba. Facultad de Psicología; Argentina.
Fil: Godoy, Juan Carlos. Consejo Nacional de Investigaciones Científicas y Tecnológicas. Instituto de Investigaciones Psicológicas; Argentina.
Fil: Haufe, Stefan. Technische Universität Berlin. Faculty IV Electrical Engineering and Computer Science. Chair of Uncertainty. Institute of Software Engineering and Theoretical Computer Science. Inverse Modeling and Machine Learning; Germany.
Fil: Haufe, Stefan. Berlin Center for Advanced Neuroimaging (BCAN), Charité – Universitätsmedizin Berlin; Germany.
Fil: Haufe, Stefan. Working Group 8.44 Machine Learning and Uncertainty. Mathematical Modelling and Data Analysis Department. Physikalisch-Technische Bundesanstalt Braunschweig und Berlin; Germany.
Fil: Haufe, Stefan. Institute for Medical Informatics, Charité – Universitätsmedizin Berlin; Germany.
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.
info:eu-repo/semantics/publishedVersion
Fil: López Steinmetz, Lorena Cecilia. Universidad Nacional de Córdoba. Facultad de Psicología; Argentina.
Fil: López Steinmetz, Lorena Cecilia. Consejo Nacional de Investigaciones Científicas y Tecnológicas. Instituto de Investigaciones Psicológicas; Argentina.
Fil: López Steinmetz, Lorena Cecilia. Technische Universität Berlin. Faculty IV Electrical Engineering and Computer Science. Chair of Uncertainty. Institute of Software Engineering and Theoretical Computer Science. Inverse Modeling and Machine Learning; Germany.
Fil: Sison, Margarita. Charité – Universitätsmedizin Berlin. Berlin Center for Advanced Neuroimaging; Germany.
Fil: Zhumagambetov, Rustam. Working Group 8.44 Machine Learning and Uncertainty. Mathematical Modelling and Data Analysis Department. Physikalisch-Technische Bundesanstalt Braunschweig und Berlin; Germany.
Fil: Godoy, Juan Carlos. Universidad Nacional de Córdoba. Facultad de Psicología; Argentina.
Fil: Godoy, Juan Carlos. Consejo Nacional de Investigaciones Científicas y Tecnológicas. Instituto de Investigaciones Psicológicas; Argentina.
Fil: Haufe, Stefan. Technische Universität Berlin. Faculty IV Electrical Engineering and Computer Science. Chair of Uncertainty. Institute of Software Engineering and Theoretical Computer Science. Inverse Modeling and Machine Learning; Germany.
Fil: Haufe, Stefan. Berlin Center for Advanced Neuroimaging (BCAN), Charité – Universitätsmedizin Berlin; Germany.
Fil: Haufe, Stefan. Working Group 8.44 Machine Learning and Uncertainty. Mathematical Modelling and Data Analysis Department. Physikalisch-Technische Bundesanstalt Braunschweig und Berlin; Germany.
Fil: Haufe, Stefan. Institute for Medical Informatics, Charité – Universitätsmedizin Berlin; Germany.