Mining Clinical Data

  • Argyris Kalogeratos
  • V. Chasanis
  • G. Rakocevic
  • A. Likas
  • Z. Babovic
  • M. Novakovic


The prerequisite of any machine learning or data mining application is to have a clear target variable that the system will try to learn. In a supervised setting, we also need to know the value of this target variable for a set of training examples (i.e., patient records). In the case study presented in this chapter, the value of the considered target variable that can be used for training is the ground truth characterizations of the coronary artery disease severity or, as a different scenario, the progression of the patients. We either set as target variable the disease severity, or disease progression, and then we consider a two-class problem in which we aim to discriminate a group of patients that are characterized as “severely diseased” or “severely progressed,” from a second group containing “mildly diseased” or “mildly progressed” patients, respectively. This latter mild/severe characterization is the actual value of the target variable for each patient.


Support Vector Machine Random Forest Left Anterior Descend Information Gain Support Vector Machine Classifier 
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 Science+Business Media New York 2013

Authors and Affiliations

  • Argyris Kalogeratos
    • 1
  • V. Chasanis
    • 1
  • G. Rakocevic
    • 2
  • A. Likas
    • 1
  • Z. Babovic
    • 3
  • M. Novakovic
    • 3
  1. 1.Department of Computer ScienceUniversity of IoanninaIoanninaGreece
  2. 2.Mathematical InstituteSerbian Academy of Sciences and ArtsBelgradeSerbia
  3. 3.Innovation Center of the School of Electrical EngineeringUniversity of BelgradeBelgradeSerbia

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