A decision tree based approach with sampling techniques to predict the survival status of poly-trauma patients
Fecha
2017Autor
Versión
Acceso abierto / Sarbide irekia
Tipo
Artículo / Artikulua
Versión
Versión publicada / Argitaratu den bertsioa
Identificador del proyecto
ES/1PE/TIN2016-77356-P
Impacto
|
10.2991/ijcis.2017.10.1.30
Resumen
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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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. [--]
Materias
Trauma patients,
Survival prediction,
Decision trees,
Imbalanced classification problems,
Sampling Techniques
Editor
Atlantis Press
Publicado en
International Journal of Computational Intelligence Systems, 2017, 10(1), 440-455
Departamento
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
Versión del editor
Entidades Financiadoras
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.