2026-04-19T18:26:12Zhttps://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

2024-02-05T08:13:13Z urn:hdl:10259/8572 Genetic Algorithms to Simplify Prognosis of Endocarditis Curiel Herrera, Leticia Elena Baruque Zanón, Bruno Dueñas, Carlos Corchado, Emilio Pérez-Tárrago, Cristina Genetic algorithms Support vector machine Infective endocarditis Feature selection algorithm Imperialist competitive algorithm 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. 2024-02-05T08:13:13Z 2024-02-05T08:13:13Z 2011 info:eu-repo/semantics/bookPart info:eu-repo/semantics/conferenceObject 978-3-642-23878-9 http://hdl.handle.net/10259/8572 10.1007/978-3-642-23878-9_54 eng Intelligent Data Engineering and Automated Learning -- IDEAL 2011, p. 454–462 https://doi.org/10.1007/978-3-642-23878-9_54 info:eu-repo/semantics/openAccess Springer