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Improving diagnosis of acute coronary syndromes in an emergency setting: A machine learning approach

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Author

  • Michael Green

Summary, in English

Acute coronary syndrome (ACS) is the biggest people killer in the western world today. Despite well trained physicians and reliable diagnostic tools, diagnosing ACS early in the emergency departments (ED) remains a challenge. In this thesis we used machine learning, via logistic regression models and artificial neural network ensembles, to investigate the possibility of predicting ACS at an early stage using electrocardiogram data. Thorough comparisons were made to several expert physicians, currently working in the ED, to verify the models. In the context of neural networks we developed methods for the case based explanation of their decisions.

Publishing year

2008

Language

English

Document type

Dissertation

Topic

  • Biophysics

Keywords

  • ensemble
  • artificial neural network
  • machine learning
  • acute coronary syndrome
  • electrocardiogram
  • case based explanation
  • decision support system

Status

Published

Supervisor

ISBN/ISSN/Other

  • ISBN: 978-91-628-7434-6

Defence date

18 June 2008

Defence time

10:15

Defence place

Lecture hall F, Department of Theoretical Physics, Sölvegatan 14A, SE-223 62 Lund, Sweden

Opponent

  • Paulo Lisboa (Professor)