Early diagnosis of acute myocardial infarction using kernel methods

Melanie Osl, Stephan Dreiseitl

Research output: Chapter in Book/Report/Conference proceedingsConference contributionpeer-review

Abstract

Acute myocardial infarction is one of the most common cardiovascular diseases in the Western world. Fortunately, not all myocardial infarctions are fatal. By early diagnosis of acute myocardial infarction based on symptoms at a patient's presentation in the emergency department, the number of deaths may be further reduced, as life-saving actions can be taken sooner. In this paper, we investigate the application of kernel-based methods to this problem, i.e. we evaluate the performance of support vector machines and kernel logistic regression models and compare these two methods to logistic regression models in terms of discrimination and calibration. The results show that kernel-based methods have higher discriminatory power for early diagnosis of acute myocardial infarction than logistic regression models and that kernel logistic regression models have superior calibration in comparison to logistic regression models and support vector machines.

Original languageEnglish
Title of host publicationProceedings of the 8th IASTED International Conference on Biomedical Engineering, Biomed 2011
Pages175-180
Number of pages6
DOIs
Publication statusPublished - 2011
Event 8th IASTED International Conference on Biomedical Engineering - Innsbruck, Austria
Duration: 16 Feb 201118 Feb 2011

Publication series

NameProceedings of the 8th IASTED International Conference on Biomedical Engineering, Biomed 2011

Conference

Conference 8th IASTED International Conference on Biomedical Engineering
CountryAustria
CityInnsbruck
Period16.02.201118.02.2011

Keywords

  • Acute myocardial infarction
  • Calibration
  • Kernel logistic regression
  • Kernel-based methods
  • Logistic regression
  • Support vector machine

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