Use of statistical classifiers as support tools for the diagnosis of iron-deficiency anemia in patients on chronic hemodialysis

P. Baiardi, V. Piazza, G. Montagna, M. C. Mazzoleni

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

Abstract

Discriminant analysis, logistic regression and neural network models were applied to the diagnosis of iron-deficiency anemia in hemodialyzed patients. The ability of the three quantitative approaches to distinguish between subjects suffering or not from iron-deficiency anemia was compared by re-substitution and cross-validation testing. Methods performance was evaluated by means of sensitivity, specificity and accuracy. All the methods performed globally well (sensitivity and specificity>0.85), revealing that the problem is classifiable. Neural networks showed the highest accuracy, both in the re-substitution (models developed and tested on the complete data set) and 3-way cross-validation (data set randomly splitted into 3 developmental and validation data sets) testing. These preliminary results suggest that the correct classification of iron status in the hemodialytic population can be treated as a pattern classification problem, for which neural networks and traditional statistical modelling can be a valuable aid to the clinical diagnosis of iron-deficiency anemia. A better performance of the neural network model must be confirmed through prospective testing on a larger data set.

Original languageEnglish
Title of host publicationStudies in Health Technology and Informatics
Pages666-670
Number of pages5
Volume43
DOIs
Publication statusPublished - 1997
Event14th Conference on Medical Informatics Europe 1997, MIE 1997 - Thessaloniki, Greece
Duration: May 25 1997May 29 1997

Other

Other14th Conference on Medical Informatics Europe 1997, MIE 1997
CountryGreece
CityThessaloniki
Period5/25/975/29/97

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management

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