CLASSIFICATION FOR SYMPTOMS OF DEPRESSION, ANXIETY AND STRESS IN STUDENTS DURING THE FIRST COVID-19 LOCKDOWN

Iliou Theodoros, Georgia Konstantopoulou

Abstract


This study presents a comprehensive analysis of the classification of depression, anxiety, and stress levels among students during the COVID-19 pandemic lockdown in Greece using machine learning methods. Leveraging a dataset derived from 1016 responses to the Dass21 questionnaire, this research evaluates the efficacy of five classifiers, IBk (KNN=3), Random Forest, MLP, FURIA, and SMO—in categorizing individuals' mental health status. The findings underscore the potential of machine learning in psychiatric evaluation and the importance of early detection and tailored interventions in mental health care.

 

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classification, depression, anxiety, stress. students, COVID-19, lockdown

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References


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DOI: http://dx.doi.org/10.46827/ejoe.v10i1.5898

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