Genetic algorithms to simplify prognosis of endocarditis

Authors:
Leticia Curiel;Bruno Baruque;Carlos Dueñas;Emilio Corchado;Cristina Pérez-Tárrago
Affiliations:
Department of Civil Engineering, University of Burgos, Burgos, Spain;Department of Civil Engineering, University of Burgos, Burgos, Spain;Complejo Hospitalario Asistencial Universitario de Burgos (SACYL), Servicio de Medicina Interna, Burgos, Spain;Departamento de Informática y Automática, Universidad de Salamanca, Salamanca, Spain;Complejo Hospitalario Asistencial Universitario de Burgos (SACYL), Servicio de Medicina Interna, Burgos, Spain
Venue:
IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning
Year:
2011

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Abstract

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.