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Two-Stage Classifier for Diagnosis of Hypertension Type

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Biological and Medical Data Analysis (ISBMDA 2006)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 4345))

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Abstract

The inductive learning approach could be immensely useful as the method generating effective classifiers. This paper presents idea of constructing two-stage classifier for diagnosis of the type of hypertension (essential hypertension and five type of secondary one: fibroplastic renal artery stenosis, atheromatous renal artery stenosis, Conn’s syndrome, renal cystic disease and pheochromocystoma). The first step decides if patient suffers from essential hypertension or secondary one. This decision is made on the base on the decision of classifier obtained by boosted version of additive tree algorithm. The second step of classification decides which type of secondary hypertension patient is suffering from. The second step of classifier makes its own decision using human expert rules. The decisions of these classifiers are made only on base on blood pressure, general information and basis biochemical data.

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Author information

Authors and Affiliations

  1. Chair of Systems and Computer Networks, Wroclaw University of Technology, Wybrzeze Wyspianskiego 27, 50-370, Wroclaw, Poland

    Michal Wozniak

Authors
  1. Michal Wozniak
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Editor information

Editors and Affiliations

  1. The Medical School, Lab of Medical Informatics, Aristotle University, Box 323, 54124, Thessaloniki, Greece

    Nicos Maglaveras  & Ioanna Chouvarda  & 

  2. Laboratory of Medical Informatics, Faculty of Medicine, Aristotle University of Thessaloniki, P.O.Box 323, 54124, Thessaloniki, Greece

    Vassilis Koutkias

  3. Institute of Informatics, Johann Wolfgang Goethe-Universität, 60054, Frankfurt a.M., Germany

    Rüdiger Brause

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Wozniak, M. (2006). Two-Stage Classifier for Diagnosis of Hypertension Type. In: Maglaveras, N., Chouvarda, I., Koutkias, V., Brause, R. (eds) Biological and Medical Data Analysis. ISBMDA 2006. Lecture Notes in Computer Science(), vol 4345. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11946465_39

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  • DOI: https://doi.org/10.1007/11946465_39

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-68063-5

  • Online ISBN: 978-3-540-68065-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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