Citation

Turska E, Jurga S, Piskorski J. Entropy (Basel) 2021; 23(9).

Copyright

(Copyright © 2021, MDPI: Multidisciplinary Digital Publications Institute)

DOI

10.3390/e23091210

PMID

34573835

Abstract

We apply tree-based classification algorithms, namely the classification trees, with the use of the rpart algorithm, random forests and XGBoost methods to detect mood disorder in a group of 2508 lower secondary school students. The dataset presents many challenges, the most important of which is many missing data as well as the being heavily unbalanced (there are few severe mood disorder cases). We find that all algorithms are specific, but only the rpart algorithm is sensitive; i.e., it is able to detect cases of real cases mood disorder. The conclusion of this paper is that this is caused by the fact that the rpart algorithm uses the surrogate variables to handle missing data. The most important social-studies-related result is that the adolescents' relationships with their parents are the single most important factor in developing mood disorders-far more important than other factors, such as the socio-economic status or school success.

Language: en

Keywords

classification; Burns test; classification trees; emotions detection; mood disorders; random forests; rpart algorithm; XGBoost