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

*Corresponding author for this work

Research output: Chapter in BookChapterResearchpeer-review

1 Citation (Scopus)


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.

Original languageEnglish
Title of host publicationMachine Learning, Optimization, and Data Science - 5th International Conference, LOD 2019, Proceedings
EditorsGiuseppe Nicosia, Panos Pardalos, Renato Umeton, Giovanni Giuffrida, Vincenzo Sciacca
Number of pages12
Publication statusPublished - 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11943 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


  • Area Under the Precision-Recall curve
  • Bayesian Classifier
  • Ensemble
  • ICU
  • Majority vote
  • Vital prognosis


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