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Vital Prognosis of Patients in Intensive Care Units Using an Ensemble of Bayesian Classifiers

Rosario Delgado, J. David Núñez-González*, J. Carlos Yébenes, Ángel Lavado

*Autor correspondiente de este trabajo

Producción científica: Capítulo de libroCapítuloInvestigaciónrevisión exhaustiva

Resumen

An Ensemble of Bayesian Classifiers (EBC) is constructed to perform vital prognosis of patients in the Intensive Care Units (ICU). The data are scarce and unbalanced, so that the size of the minority class (critically ill patients who die) is very small, and this fact prevents the use of accuracy as a measure of performance in classification; instead we use the Area Under the Precision-Recall curve (AUPR). To address the classification in this setting, we propose the use of an ensemble constructed from five base Bayesian classifiers with the weighted majority vote rule, where the weights are defined from AUPR. We compare this EBC model with the base Bayesian classifiers used to build it, as well as with the ensemble obtained using the mere majority vote criterion, and with some state-of-the-art machine learning supervised classifiers. Our results show that the EBC model outperforms most of the competing classifiers, being only slightly surpassed by Random Forest.

Idioma originalInglés
Título de la publicación alojadaMachine Learning, Optimization, and Data Science - 5th International Conference, LOD 2019, Proceedings
EditoresGiuseppe Nicosia, Panos Pardalos, Renato Umeton, Giovanni Giuffrida, Vincenzo Sciacca
Lugar de publicaciónCham
Páginas619-630
Número de páginas12
DOI
EstadoPublicada - 2019
Evento5th International Conference on Machine Learning, Optimization, and Data Science, LOD 2019 - Siena, Italia
Duración: 10 sept 201913 sept 2019

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen11943 LNCS
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conferencia

Conferencia5th International Conference on Machine Learning, Optimization, and Data Science, LOD 2019
País/TerritorioItalia
CiudadSiena
Período10/09/1913/09/19

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