A decision tree based approach with sampling techniques to predict the survival status of poly-trauma patients
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Date
2017Author
Version
Acceso abierto / Sarbide irekia
Type
Artículo / Artikulua
Version
Versión publicada / Argitaratu den bertsioa
Project Identifier
ES/1PE/TIN2016-77356-P
Impact
Abstract
Survival prediction of poly-trauma patients measure the quality of emergency services by comparing their predictions with the real outcomes. The aim of this paper is to tackle this problem applying C4.5 since it
achieves accurate results and it provides interpretable models. Furthermore, we use sampling techniques because, among the 378 patients treated at the Hospital of Navarre, the number of ...
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Subject
Trauma patients,
Survival prediction,
Decision trees,
Imbalanced classification problems,
Sampling Techniques
Publisher
Atlantis Press
Published in
International Journal of Computational Intelligence Systems, 2017, 10(1), 440-455
Departament
Universidad Pública de Navarra. Departamento de Automática y Computación /
Nafarroako Unibertsitate Publikoa. Automatika eta Konputazioa Saila /
Universidad Pública de Navarra. Departamento de Ciencias de la Salud /
Nafarroako Unibertsitate Publikoa. Osasun Zientziak Saila /
Universidad Pública de Navarra/Nafarroako Unibertsitate Publikoa. Institute of Smart Cities - ISC
Publisher version
Sponsorship
This work was supported in part by the Spanish Ministry of Science and Technology under Projects TIN2016-77356-P and by the Health Department of the Navarre Government under Project PI-019/11.
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Except where otherwise noted, this item's license is described as © 2017, the Authors. This is an open access article under the CC BY-NC license.
