2024-03-29T07:34:57Zhttps://gredos.usal.es/oai/requestoai:gredos.usal.es:10366/1348872022-02-07T15:35:50Zcom_10366_122575com_10366_4512com_10366_3823col_10366_134811
Genetic Algorithms to Simplify Prognosis of Endocarditis
Curiel, Leticia
Baruque, Bruno
Dueñas, Carlos
Corchado Rodríguez, Emilio Santiago
Pérez Tárrago, Cristina
Computer Science
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.
2017-09-06T09:14:32Z
2017-09-06T09:14:32Z
2011
info:eu-repo/semantics/article
Intelligent Data Engineering and Automated Learning - IDEAL 2011 Lecture Notes in Computer Science. Lecture Notes in Computer Science. Volumen 6936, pp. 454-462.
978-3-642-23877-2 (Print) / 978-3-642-23878-9 (Online)
0302-9743 (Print) / 1611-3349 (Online)
http://hdl.handle.net/10366/134887
en
https://creativecommons.org/licenses/by-nc-nd/3.0/
info:eu-repo/semantics/openAccess
Attribution-NonCommercial-NoDerivs 3.0 Unported
Springer Science + Business Media