Using a Classifier Ensemble for Preventing Burnout in University Students: A Study Case in Valparaíso

Abstract

University students are constantly exposed to high-tension situations and peaks of stress caused by the difficulty from their many responsibilities. These situations can produce disorders in students, such as psychosomatic, behavioral, or emotional disorders. Burnout is a work-related syndrome that can be observed in students. This syndrome considers depersonalization, emotional exhaustion, and diminished feelings of personal accomplishment. We aim to detect anxiety, depression, and burnout symptoms in university students to prevent further negative consequences. For this, we design a questionnaire using well-known instruments to detect these signs. We propose to use an ensemble of classifiers, including random forest and artificial neural networks, to predict a set of four possible disturbances in persons. The proposal will be used in the Human Place project to suggest strategies to tackle four disturbance types. This study considers the participation of 93 persons from the Valparaiso region in Chile. Results show that the evaluated algorithms can predict the presence or absence of the disturbances with high accuracy levels and a low number of false negative cases. We also present a detailed analysis of which questions were relevant in the classification task of each algorithm.