RT info:eu-repo/semantics/bookPart
T1 Complications Detection in Treatment for Bacterial Endocarditis
A1 Curiel Herrera, Leticia Elena
A1 Baruque Zanón, Bruno
A1 Dueñas, Carlos
A1 Corchado, Emilio
A1 Pérez, Cristina
K1 Decision Tree
K1 Infective Endocarditis
K1 Bacterial Endocarditis
K1 Kernel Cluster
K1 Case Base Reasoning System
K1 Informática
K1 Computer science
K1 Medicina
K1 Medicine
AB This study proposes the use of decision trees to detect possible complications in a critical disease called endocarditis. The endocarditis illness could produce heart failure, stroke, kidney failure, emboli, immunological disorders anddeath. The aim is to obtained a tree decision classifier based on the symptoms (attributes) of patients (the data instances) observed by doctors to predict the possiblecomplications that can occur when a patient is in treatment of bacterial endocarditis and thus, help doctors to make an early diagnose so that they can treat more effectively the infection and aid to a patient faster recovery. The results obtainedusing a real data set, show that with the information extracted form each case in anearly stage of the development of the patient a quite accurate idea of the complications that can arise can be extracted.
PB Springer Nature
SN 1867-5662
YR 2011
FD 2011
LK http://hdl.handle.net/10259/8577
UL http://hdl.handle.net/10259/8577
LA eng
NO Trabajo presentado en: International Symposium on Distributed Computing and Artificial Intelligence, 2011
DS Repositorio Institucional de la Universidad de Burgos
RD 10-may-2024