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

Michael Green

Research output: ThesisDoctoral Thesis (compilation)

104 Downloads (Pure)


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.
Original languageEnglish
Awarding Institution
  • Computational Biology and Biological Physics
  • Ohlsson, Mattias, Supervisor
  • Edenbrandt, Lars, Supervisor
Award date2008 Jun 18
ISBN (Print)978-91-628-7434-6
Publication statusPublished - 2008

Bibliographical note

Defence details

Date: 2008-06-18
Time: 10:15
Place: Lecture hall F, Department of Theoretical Physics, Sölvegatan 14A, SE-223 62 Lund, Sweden

External reviewer(s)

Name: Lisboa, Paulo
Title: Professor
Affiliation: Liverpool John Moores University, Liverpool, England


Subject classification (UKÄ)

  • Biophysics


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


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