Effects of data transformation methods on classification of patients diagnosed with myocardial infarction

Saeed Mehrabi, Iman Mohammadi, Kislaya Kunjan, Hadi Kharrazi

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

Abstract

Large datasets may contain redundant data. Variable selection methods that select most relevant variables in the data set, fail to consider the interaction between the variables. Data transformation methods are used to transfer the original data to a new dimension and capture the most significant information within the data set. The data set used in this study was based on 45 clinical variables collected from 697 patients diagnosed as either having myocardial infarction (MI) or not. Principal component analysis (PCA) and independent component analysis (ICA) were applied prior to classification of patients to MI or Non-MI groups using support vector machines (SVM).

Original languageEnglish (US)
Title of host publicationMEDINFO 2013 - Proceedings of the 14th World Congress on Medical and Health Informatics
PublisherIOS Press
Pages1203
Number of pages1
Edition1-2
ISBN (Print)9781614992882
DOIs
StatePublished - 2013
Event14th World Congress on Medical and Health Informatics, MEDINFO 2013 - Copenhagen, Denmark
Duration: Aug 20 2013Aug 23 2013

Publication series

NameStudies in Health Technology and Informatics
Number1-2
Volume192
ISSN (Print)0926-9630
ISSN (Electronic)1879-8365

Other

Other14th World Congress on Medical and Health Informatics, MEDINFO 2013
Country/TerritoryDenmark
CityCopenhagen
Period8/20/138/23/13

Keywords

  • Decision support system
  • ICA
  • PCA
  • SVM

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management

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