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

00925njm 22002777a 4500 dc Curiel Herrera, Leticia Elena author Baruque Zanón, Bruno author Dueñas, Carlos author Corchado, Emilio author Pérez, Cristina author 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. 1867-5662 http://hdl.handle.net/10259/8577 10.1007/978-3-642-19934-9_30 1867-5670 Decision Tree Infective Endocarditis Bacterial Endocarditis Kernel Cluster Case Base Reasoning System Complications Detection in Treatment for Bacterial Endocarditis