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- W184272309 abstract "AbstractThis 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.KeywordsDecision TreeInfective EndocarditisBacterial EndocarditisKernel ClusterCase Base Reasoning SystemThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves." @default.
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- W184272309 date "2011-01-01" @default.
- W184272309 modified "2023-10-01" @default.
- W184272309 title "Complications Detection in Treatment for Bacterial Endocarditis" @default.
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- W184272309 doi "https://doi.org/10.1007/978-3-642-19934-9_30" @default.
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