2021
DOI: 10.3390/e23091210
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Mood Disorder Detection in Adolescents by Classification Trees, Random Forests and XGBoost in Presence of Missing Data

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 case… Show more

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Cited by 6 publications
(2 citation statements)
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“…Although most machine learning techniques can only be used to impute missing data or to employ CCA by default [29], XGBoost [37,38], a modern version of the gradientboosting technique, has crafted features that can autonomously manage missing data. XGBoost addresses the issue of missing values by including a pre-set path for missing data in each tree split.…”
Section: Related Workmentioning
confidence: 99%
“…Although most machine learning techniques can only be used to impute missing data or to employ CCA by default [29], XGBoost [37,38], a modern version of the gradientboosting technique, has crafted features that can autonomously manage missing data. XGBoost addresses the issue of missing values by including a pre-set path for missing data in each tree split.…”
Section: Related Workmentioning
confidence: 99%
“…Random forest is an algorithm based on the bagging algorithm [22]. The random forest is one of the ensembles learning methods that consist of a set of decision trees that are generated by the recursive sampling of bootstrapped samples of training data [23].…”
Section: -3-random Forestmentioning
confidence: 99%