[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123935-en":3,"doc-seo-123935-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123935,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","Machine learning models predict the emergence of depression in Argentinean college students during periods of COVID-19 quarantine","The study develops and compares data-driven machine learning models to predict depression emergence among Argentinean college students during COVID-19 quarantine periods. A longitudinal dataset of 1,492 students provides T1 and T2 measurements, with features covering baseline depression/anxiety, mental disorder and suicidal behavior history, quarantine sub-period context, sex, and age. Classification and regression performance are evaluated using multiple metrics, and key predictive feature contributions are examined via univariate analyses. Results identify SVM and logistic regression as top performers, showing strong potential for early detection of at-risk students, pending further validation before clinical use.","TYPE Original Research PUBLISHED 16 April 2024  \nDOI 10.3389/fpsyt.2024.1376784  \nOPEN ACCESS  \nEDITED BY Jianjun Ou,  \nThe Second Xiangya Hospital of Central South University, China  \nREVIEWED BY  \nCaglar Uyulan,  \nIzmir Kˆatip C¸elebi University, Türkiye Seyed-Ali Sadegh-Zadeh,  \nStaffordshire University, United Kingdom  \n*CORRESPONDENCE  \nLorena Cecilia Lo´pez Steinmetz  \n [lopez.steinmetz@tu-berlin.de](lopez.steinmetz@tu-berlin.de);  \n [cecilialopezsteinmetz@unc.edu.ar](cecilialopezsteinmetz@unc.edu.ar)[ ](cecilialopezsteinmetz@unc.edu.ar)Stefan Haufe  \n [haufe@tu-berlin.de](haufe@tu-berlin.de)  \n†These authors have contributed equally to this work  \nRECEIVED 26 January 2024  \nACCEPTED 29 March 2024  \nPUBLISHED 16 April 2024  \nCITATION  \nLo´pez Steinmetz LC, Sison M, Zhumagambetov R, Godoy JC  \nand Haufe S (2024) Machine learning models predict the emergence of depression in Argentinean college students during periods of COVID-19 quarantine.  \nFront. Psychiatry 15:1376784 .  \ndoi: 10.3389/fpsyt.2024.1376784  \nCOPYRIGHT  \n© 2024 López Steinmetz, Sison, Zhumagambetov, Godoy and Haufe. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nMachine learning models predict the emergence of depression in Argentinean college students during periods of COVID-  \n19 quarantine  \nLorena Cecilia Lo´ pez Steinmetz 1,2*, Margarita Sison 3†, Rustam Zhumagambetov 4†,  \nJuan Carlos Godoy2 and Stefan Haufe 1,3,4,5*  \n1 Inverse 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, Germany, 2 Instituto de Investigaciones Psicológicas (IIPsi), Facultad de Psicología, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Universidad Nacional de Córdoba (UNC), Co´rdoba, Argentina, 3 Berlin Center for Advanced Neuroimaging (BCAN), Charité – Universitätsmedizin Berlin, Berlin, Germany, 4Working Group 8 .44 Machine Learning and Uncertainty, Mathematical Modelling and Data Analysis Department, Physikalisch-Technische Bundesanstalt Braunschweig und Berlin, Berlin, Germany, 5 Institute for Medical Informatics, Charité – Universitätsmedizin Berlin, Berlin, Germany  \nIntroduction: 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.  \nAims: 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.  \n3) To identify key features driving depression prediction.  \nMethods: 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-squa","cbCaihTPSs8QiXCX","https://ap.wps.com/l/cbCaihTPSs8QiXCX","pdf",3957528,1,15,"English","en",105,"# Introduction\n## Aims\n## Methods\n## Results\n## Discussion","[{\"question\":\"What is the main goal of the study?\",\"answer\":\"To develop and compare machine learning models that predict the emergence of depression in Argentinean college students during COVID-19 quarantine, and to evaluate classification and regression performance and key predictive features.\"},{\"question\":\"What data and features are used to build the models?\",\"answer\":\"The models use a longitudinal dataset of 1,492 students with T1 and T2 measurements, including baseline depression and anxiety scores, mental disorder/suicidal behavior history, quarantine sub-period information, sex, and age.\"},{\"question\":\"Which machine learning methods perform best and how is performance measured?\",\"answer\":\"SVM and logistic regression show the highest classification performance, while SVR and ridge regression perform strongly for regression. Classification uses metrics such as AUPRC and balanced accuracy, and regression uses R-squared and error measures.\"}]","Machine learning models predict the emergence of depression in Argentinean college students during periods of COVID-19 quarantine | PDF",1785819320,38,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-models-predict-the-emergence-of-depression-in-argentinean-college-students-during-periods-of-covid-19-quarantine","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-models-predict-the-emergence-of-depression-in-argentinean-college-students-during-periods-of-covid-19-quarantine/123935/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main goal of the study?","Question",{"text":75,"@type":76},"To develop and compare machine learning models that predict the emergence of depression in Argentinean college students during COVID-19 quarantine, and to evaluate classification and regression performance and key predictive features.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data and features are used to build the models?",{"text":80,"@type":76},"The models use a longitudinal dataset of 1,492 students with T1 and T2 measurements, including baseline depression and anxiety scores, mental disorder/suicidal behavior history, quarantine sub-period information, sex, and age.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning methods perform best and how is performance measured?",{"text":84,"@type":76},"SVM and logistic regression show the highest classification performance, while SVR and ridge regression perform strongly for regression. 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