Genetic Algorithms to Simplify Prognosis of Endocarditis
Fecha de publicación
Springer Science + Business Media
Intelligent Data Engineering and Automated Learning - IDEAL 2011 Lecture Notes in Computer Science. Lecture Notes in Computer Science. Volumen 6936, pp. 454-462.
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.
978-3-642-23877-2 (Print) / 978-3-642-23878-9 (Online)
0302-9743 (Print) / 1611-3349 (Online)
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