Diagnosis of iron-deficiency anemia in hemodialyzed patients through support vector machines technique

Paola Baiardi, Valter Piazza, Maria C. Mazzoleni

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages144-147
Number of pages4
Volume2101
ISBN (Print)3540422943, 9783540422945
Publication statusPublished - 2001
Event8th Conference on Artificial Intelligence in Medicine in Europe, AIME 2001 - Cascais, Portugal
Duration: Jul 1 2001Jul 4 2001

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2101
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other8th Conference on Artificial Intelligence in Medicine in Europe, AIME 2001
CountryPortugal
CityCascais
Period7/1/017/4/01

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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