2026-04-19T16:42:55Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/85722024-02-06T01:05:24Zcom_10259_3847com_10259_5086com_10259_2604col_10259_7109col_10259_7409

Curiel Herrera, Leticia Elena 824 600 Baruque Zanón, Bruno 54 600 0000-0002-4993-204X Dueñas, Carlos 88a744fb-e727-4027-a7d7-e02c5c3e1693 600 Corchado, Emilio 125 600 0000-0001-8560-3991 Pérez-Tárrago, Cristina 57dbfce3-26c0-4bfb-8493-a91ed3b7b9e5 600 2024-02-05T08:13:13Z 2024-02-05T08:13:13Z 2011 978-3-642-23878-9 http://hdl.handle.net/10259/8572 10.1007/978-3-642-23878-9_54 Trabajo presentado en: 12th International Conference, Norwich, UK, September 7-9, 2011. Proceedings This ongoing interdisciplinary research is based on the application of genetic algorithms to simplify the process of predicting the mortality of a critical illness called endocarditis. The goal is to determine the most relevant features (symptoms) of patients (samples) observed by doctors to predict the possible mortality once the patient is in treatment of bacterial endocarditis. This can help doctors to prognose the illness in early stages; by helping them to identify in advance possible solutions in order to aid the patient recover faster. The results obtained using a real data set, show that using only the features selected by employing a genetic algorithm from each patient’s case can predict with a quite high accuracy the most probable evolution of the patient. We would like to extend our thanks to Complejo Hospitalario Asistencial Universitario de Burgos (SACYL). This research has been partially supported through projects TIN2010-21272-C02-01 from the Spanish Ministry of Science and Innovation and Grupo Antolin Ingenieria, S.A., within the framework of project MAGNO2008 - 1028.- CENIT. application/pdf eng Springer Intelligent Data Engineering and Automated Learning -- IDEAL 2011, p. 454–462 https://doi.org/10.1007/978-3-642-23878-9_54 Genetic algorithms Support vector machine Infective endocarditis Feature selection algorithm Imperialist competitive algorithm Informática Medicina Computer science Medicine Genetic Algorithms to Simplify Prognosis of Endocarditis info:eu-repo/semantics/bookPart info:eu-repo/semantics/conferenceObject info:eu-repo/semantics/acceptedVersion info:eu-repo/semantics/openAccess THUMBNAIL Curiel-Genetic_algorithms_to_simplify_prognosis_of_endocarditis.pdf.jpg Curiel-Genetic_algorithms_to_simplify_prognosis_of_endocarditis.pdf.jpg IM Thumbnail image/jpeg 3974 https://riubu.ubu.es/bitstream/10259/8572/3/Curiel-Genetic_algorithms_to_simplify_prognosis_of_endocarditis.pdf.jpg 8ec7160293eb560716b03b9f35d39379 MD5 3 LICENSE license.txt license.txt text/plain; charset=utf-8 1362 https://riubu.ubu.es/bitstream/10259/8572/2/license.txt 5d013bfa6e473ff0db22cd82a4d71a70 MD5 2 ORIGINAL Curiel-Genetic_algorithms_to_simplify_prognosis_of_endocarditis.pdf Curiel-Genetic_algorithms_to_simplify_prognosis_of_endocarditis.pdf application/pdf 192180 https://riubu.ubu.es/bitstream/10259/8572/1/Curiel-Genetic_algorithms_to_simplify_prognosis_of_endocarditis.pdf 6408d669ed4b21c7cefff4209e5e7c6a MD5 1 10259/8572 oai:riubu.ubu.es:10259/8572 2024-02-06 02:05:24.536 Repositorio Institucional de la Universidad de Burgos bubrep@ubu.es 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