2026-11-02T10:26:50Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/85772024-02-08T01:05:13Zcom_10259_3847com_10259_5086com_10259_2604col_10259_7109col_10259_7409

Curiel Herrera, Leticia Elena Baruque Zanón, Bruno Dueñas, Carlos Corchado, Emilio Pérez, Cristina 2024-02-05T10:48:22Z 2024-02-05T10:48:22Z 2011 1867-5662 http://hdl.handle.net/10259/8577 10.1007/978-3-642-19934-9_30 1867-5670 Trabajo presentado en: International Symposium on Distributed Computing and Artificial Intelligence, 2011 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 and death. 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 possible complications 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 obtained using a real data set, show that with the information extracted form each case in an early stage of the development of the patient a quite accurate idea of the complications that can arise can be extracted. application/pdf eng Springer Nature Advances in Intelligent and Soft Computing. 2011, V. 91, p. 241-248 https://doi.org/10.1007/978-3-642-19934-9_30 Decision Tree Infective Endocarditis Bacterial Endocarditis Kernel Cluster Case Base Reasoning System Informática Medicina Computer science Medicine Complications Detection in Treatment for Bacterial Endocarditis info:eu-repo/semantics/bookPart info:eu-repo/semantics/conferenceObject info:eu-repo/semantics/acceptedVersion info:eu-repo/semantics/openAccess 91 241 248