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Transactions on Rough Sets VII pp 70–84Cite as

Selection of Important Attributes for Medical Diagnosis Systems

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Part of the book series: Lecture Notes in Computer Science ((TRS,volume 4400))

Abstract

Success of machine learning algorithms is usually dependent on a quality of a dataset they operate on. For datasets containing noisy, inadequate or irrelevant information these algorithms may produce less accurate results. Therefore a common pre-processing step in data mining domain is a selection of highly predictive attributes. In this case study we select subsets of attributes from medical data using filter feature selection algorithms. To validate the algorithms we induce decision rules from the selected subsets of attributes and compare classification accuracy on both training and test datasets. Additionally medical relevance of the selected attributes is checked with help of domain experts.

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Author information

Authors and Affiliations

  1. Siemens AG Medical Solutions, Allee am Roethelheimpark 2, 91052 Erlangen, Germany

    Grzegorz Ilczuk

  2. Institut of Informatics University of Silesia, Bedzinska 39, 41-200 Sosnowiec, Poland

    Alicja Wakulicz-Deja

Authors
  1. Grzegorz Ilczuk
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  2. Alicja Wakulicz-Deja
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Editor information

James F. Peters Andrzej Skowron Victor W. Marek Ewa Orłowska Roman Słowiński Wojciech Ziarko

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Ilczuk, G., Wakulicz-Deja, A. (2007). Selection of Important Attributes for Medical Diagnosis Systems. In: Peters, J.F., Skowron, A., Marek, V.W., Orłowska, E., Słowiński, R., Ziarko, W. (eds) Transactions on Rough Sets VII. Lecture Notes in Computer Science, vol 4400. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71663-1_5

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  • DOI: https://doi.org/10.1007/978-3-540-71663-1_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-71662-4

  • Online ISBN: 978-3-540-71663-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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