Intelligent Data Engineering and Automated Learning -- IDEAL 2011, p. 454–462
Editorial
Springer
Fecha de publicación
2011
ISBN
978-3-642-23878-9
DOI
10.1007/978-3-642-23878-9_54
Descripción
Trabajo presentado en: 12th International Conference, Norwich, UK, September 7-9, 2011. Proceedings
Resumen
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.
