A decision tree based approach with sampling techniques to predict the survival status of poly-trauma patients
| dc.contributor.author | Sanz Delgado, José Antonio | |
| dc.contributor.author | Fernández Fernández, Francisco Javier | |
| dc.contributor.author | Bustince Sola, Humberto | |
| dc.contributor.author | Gradín Purroy, Carlos | |
| dc.contributor.author | Belzunegui Otano, Tomás | |
| dc.contributor.department | Automatika eta Konputazioa | eu |
| dc.contributor.department | Osasun Zientziak | eu |
| dc.contributor.department | Institute of Smart Cities - ISC | en |
| dc.contributor.department | Automática y Computación | es_ES |
| dc.contributor.department | Ciencias de la Salud | es_ES |
| dc.contributor.funder | Gobierno de Navarra / Nafarroako Gobernua, PI-019/11 | es |
| dc.date.accessioned | 2020-10-15T11:31:40Z | |
| dc.date.available | 2020-10-15T11:31:40Z | |
| dc.date.issued | 2017 | |
| dc.description.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. | en |
| dc.description.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. | en |
| dc.format.extent | 16 p. | |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.doi | 10.2991/ijcis.2017.10.1.30 | |
| dc.identifier.issn | 1875-6883 | |
| dc.identifier.uri | https://academica-e.unavarra.es/handle/2454/38426 | |
| dc.language.iso | eng | en |
| dc.publisher | Atlantis Press | en |
| dc.relation.ispartof | International Journal of Computational Intelligence Systems, 2017, 10(1), 440-455 | en |
| dc.relation.projectID | info:eu-repo/grantAgreement/ES/1PE/TIN2016-77356-P/ | |
| dc.relation.publisherversion | https://doi.org/10.2991/ijcis.2017.10.1.30 | |
| dc.rights | © 2017, the Authors. This is an open access article under the CC BY-NC license. | en |
| dc.rights.accessRights | info:eu-repo/semantics/openAccess | |
| dc.rights.uri | https://creativecommons.org/licenses/by-nc/4.0/ | |
| dc.subject | Trauma patients | en |
| dc.subject | Survival prediction | en |
| dc.subject | Decision trees | en |
| dc.subject | Imbalanced classification problems | en |
| dc.subject | Sampling Techniques | en |
| dc.title | A decision tree based approach with sampling techniques to predict the survival status of poly-trauma patients | en |
| dc.type | info:eu-repo/semantics/article | |
| dc.type.version | info:eu-repo/semantics/publishedVersion | |
| dspace.entity.type | Publication | |
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