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Mining Clinical Data

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Computational Medicine in Data Mining and Modeling

Abstract

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.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Ioannina, GR-45110, Ioannina, Greece

    Argyris Kalogeratos, V. Chasanis & A. Likas

  2. Mathematical Institute, Serbian Academy of Sciences and Arts, Belgrade, 11000, Serbia

    G. Rakocevic

  3. Innovation Center of the School of Electrical Engineering, University of Belgrade, Belgrade, 11000, Serbia

    Z. Babovic & M. Novakovic

Authors
  1. Argyris Kalogeratos
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  2. V. Chasanis
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  3. G. Rakocevic
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  4. A. Likas
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  5. Z. Babovic
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  6. M. Novakovic
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Corresponding author

Correspondence to Argyris Kalogeratos .

Editor information

Editors and Affiliations

  1. Mathematical Institute, Serbian Academy of Science and Arts, Belgrade, Serbia

    Goran Rakocevic

  2. Faculty of Engineering, University of Kragujevac, Kragujevac, Serbia

    Tijana Djukic

  3. Faculty of Engineering, University of Kragujevac, Kragujevac, Serbia

    Nenad Filipovic

  4. School of Electrical Engineering, University of Belgrade, Belgrade, Serbia

    Veljko Milutinović

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Kalogeratos, A., Chasanis, V., Rakocevic, G., Likas, A., Babovic, Z., Novakovic, M. (2013). Mining Clinical Data. In: Rakocevic, G., Djukic, T., Filipovic, N., Milutinović, V. (eds) Computational Medicine in Data Mining and Modeling. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8785-2_1

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  • DOI: https://doi.org/10.1007/978-1-4614-8785-2_1

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  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4614-8784-5

  • Online ISBN: 978-1-4614-8785-2

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