Comparison of Machine Learning algorithms for the Burnout projection
This study opens up an innovative field of research by integrating resources from psychological evaluation and virtual resources to treat various implications of Burnout in school dropout and low academic performance through the analysis of information and the generation of algorithms that allow the projection of burnout risk.
Abstract
The present study aims to carry out a projection of student burnout risk detection in young university students using Machine Learning technics (Neuronal Networks, KNN, SVM, Random Forest). A descriptive method was proposed, with a cross-sectional and stratified design in which a sample of 791 students from 4 different universities. This study opens up an innovative field of research by integrating resources from psychological evaluation and virtual resources, in addition, it would allow the generation of preventive actions to treat various implications of Burnout in school dropout and low academic performance through the analysis of information and the generation of algorithms that allow the projection of burnout risk. Due to the combination of experience of professionals in psychology, education and engineering, as well as the contribution to the projection of a syndrome that affects students, makes this article an innovative proposal.