An evolutionary underbagging approach to tackle the survival prediction of trauma patients: a case study at the Hospital of Navarre
| Autor: | Humberto Bustince, José Antonio Sanz, T. Belzunegui, Mikel Galar |
|---|---|
| Přispěvatelé: | Universidad Pública de Navarra. Departamento de Estadística, Informática y Matemáticas, Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa. ISC - Institute of Smart Cities, Nafarroako Unibertsitate Publikoa. Estatistika, Informatika eta Matematika Saila, Gobierno de Navarra / Nafarroako Gobernua, PI-019/11 |
| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
General Computer Science Computer science Process (engineering) media_common.quotation_subject Evolutionary algorithm 02 engineering and technology Machine learning computer.software_genre Evolutionary algorithms Trauma 020901 industrial engineering & automation 0202 electrical engineering electronic engineering information engineering General Materials Science Quality (business) Imbalanced classification Real interest rate evolutionary algorithms Ensembles survival prediction media_common Survival prediction business.industry Mortality rate imbalanced classification General Engineering trauma 020201 artificial intelligence & image processing Artificial intelligence lcsh:Electrical engineering. Electronics. Nuclear engineering business computer lcsh:TK1-9971 |
| Zdroj: | Academica-e. Repositorio Institucional de la Universidad Pública de Navarra instname IEEE Access, Vol 7, Pp 76009-76021 (2019) Academica-e: Repositorio Institucional de la Universidad Pública de Navarra Universidad Pública de Navarra |
| Popis: | Survival prediction systems are used among emergency services at hospitals in order to measure their quality objectively. In order to do so, the estimated mortality rate given by a prediction model is compared with the real rate of the hospital. Hence, the accuracy of the prediction system is a key factor as more reliable estimations can be obtained. Survival prediction systems are aimed at scoring the severity of patients' injuries. Afterward, this score is used to estimate whether the patient will survive or not. Luckily, the number of patients who survive their injuries is greater than that of those who die. However, this degree of imbalance implies a greater difficulty in learning the prediction models. The aim of this paper is to develop a new prediction system for the Hospital of Navarre with the goal of improving the prediction capabilities of the currently used models since it would imply having a more reliable measurement of its quality. In order to do so, we propose a new strategy to conform an ensemble of classifiers using an evolutionary under sampling process in the bagging methodology. The experimental study is carried out over 462 patients who were treated at the Hospital of Navarre. Our new ensemble approach is an appropriate tool to deal with this problem as it is able to outperform the currently used models by the staff of the hospital as well as several state-of-the-art ensemble approaches designed for imbalanced domains. This work was supported in part by the Spanish Ministry of Science and Technology under Project TIN2016-77356-P (AEI/FEDER, UE), in part by the Network Project under Grant TIN2014-56381-REDT, and in part by the Health Department of the Government of Navarre under Project PI-019/11. |
| Databáze: | OpenAIRE |
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