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Diagnosis of Iron-Deficiency Anemia in Hemodialyzed Patients through Support Vector Machines Technique

  • Paola Baiardi
  • Valter Piazza
  • Maria C. Mazzoleni
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2101)

Abstract

Support Vector Machines (SVMs) technique is a recent method for empirical data modelling applied to pattern recognition problems. The aim of the present study is to test SVMs performance when applied to a specific medical classification problem — diagnosis of iron-deficiency anemia in uremic patients — and to compare the results with those obtained by traditional techniques such as logistic regression and discriminant analysis. Models have been compared both in learning and validation phases. All methods performed well (accuracy > 80%). Sensibility of SVMs is always higher than the ones of the other models; specificity and accuracy are lower in one repetition over three. Within the limits of the present study, we can say that the SVMs can constitute an innovative method to approach clinical classification problem on which to further invest.

Keywords

Support Vector Machine Iron Status Serum Ferritin Level Uremic Patient Validation Phase 
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 2001

Authors and Affiliations

  • Paola Baiardi
    • 1
  • Valter Piazza
    • 2
  • Maria C. Mazzoleni
    • 1
  1. 1.Salvatore Maugeri FoundationIRCCS, Medical Informatics UnitPaviaItaly
  2. 2.Salvatore Maugeri FoundationIRCCS, Nephrology UnitPaviaItaly

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