Complications Detection in Treatment for Bacterial Endocarditis

  • Leticia Curiel
  • Bruno Baruque
  • Carlos Dueñas
  • Emilio Corchado
  • Cristina Pérez
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 91)


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.


Decision Tree Infective Endocarditis Bacterial Endocarditis Kernel Cluster Case Base Reasoning System 
These 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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Leticia Curiel
    • 1
  • Bruno Baruque
    • 1
  • Carlos Dueñas
    • 2
  • Emilio Corchado
    • 3
  • Cristina Pérez
    • 2
  1. 1.Department of Civil EngineeringUniversity of BurgosBurgosSpain
  2. 2.Servicio de Medicina InternaComplejo Hospitalario Asistencial de BurgosBurgosSpain
  3. 3.Departamento de Informática y AutomáticaUniversidad de SalamancaSalamancaSpain

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