International Journal of Computational Intelligence Systems

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Volume 10, Issue 1, 2017, Pages 440 - 455

A decision tree based approach with sampling techniques to predict the survival status of poly-trauma patients

Authors
José Sanza, joseantonio.sanz@unavarra.es, Javier Fernandeza, Humberto Bustincea, Carlos Gradinb, Mariano Fortúnc, Tomás Belzuneguib, d
aDepartamento de Automatica y Computacion, Institute of Smart Cities, Universidad Publica de Navarra, Campus Arrosadia s/n, Pamplona, P.O. Box 31006, Spain
bDepartment of Health, Universidad Publica de Navarra, Barañain Avenue s/n, Pamplona, P.O. Box 31008, Spain
cAccident and Emergency Department, Hospital of Tudela, Carretera Tarazona, Km. 3, Tudela, Spain
dAccident and Emergency Department, Hospital of Navarre, Calle de Irunlarrea, 3E, Pamplona, Spain
Received 10 June 2016, Accepted 9 November 2016, Available Online 1 January 2017.
DOI
10.2991/ijcis.2017.10.1.30How to use a DOI?
Keywords
Trauma patients; Survival prediction; Decision trees; Imbalanced classification problems; Sampling Techniques
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 survivals excels that of deaths. Logistic regressions are used in the comparison, since they are an standard in this domain.

Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

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<Previous Article In Issue
Next Article In Issue>
Journal
International Journal of Computational Intelligence Systems
Volume-Issue
10 - 1
Pages
440 - 455
Publication Date
2017/01/01
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
10.2991/ijcis.2017.10.1.30How to use a DOI?
Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

Cite this article

risenwbib
TY  - JOUR
AU  - José Sanz
AU  - Javier Fernandez
AU  - Humberto Bustince
AU  - Carlos Gradin
AU  - Mariano Fortún
AU  - Tomás Belzunegui
PY  - 2017
DA  - 2017/01/01
TI  - A decision tree based approach with sampling techniques to predict the survival status of poly-trauma patients
JO  - International Journal of Computational Intelligence Systems
SP  - 440
EP  - 455
VL  - 10
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.2017.10.1.30
DO  - 10.2991/ijcis.2017.10.1.30
ID  - Sanz2017
ER  -