Niklas Lavesson
Blekinge Institute of Technology, Sweden
Anders Halling
Blekinge Competence Center, Sweden
Michael Freitag
Herlev Hospital, Sweden
Jacob Odeberg
Dept. of Medicine, Karolinska Institutet and University Hospital, Sweden
Håkan Odeberg
Blekinge Competence Center, Sweden
Paul Davidsson
Blekinge Institute of Technology, Sweden
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Ingår i: The Swedish AI Society Workshop May 27-28; 2009 IDA; Linköping University
Linköping Electronic Conference Proceedings 35:10, s. 55-63
Publicerad: 2009-05-27
ISBN:
ISSN: 1650-3686 (tryckt), 1650-3740 (online)
An Acute Coronary Syndrome (ACS) is a set of clinical signs and symptoms; interpreted as the result of cardiac ischemia; or abruptly decreased blood flow to the heart muscle. The subtypes of ACS include Unstable Angina (UA) and Myocardial Infarction (MI). Acute MI is the single most common cause of death for both men and women in the developed world. Several data mining studies have analyzed di erent types of patient data in order to generate models that are able to predict the severity of an ACS. Such models could be used as a basis for choosing an appropriate form of treatment. In most cases; the data is based on electrocardiograms (ECGs). In this preliminary study; we analyze a unique ACS database; featuring 28 variables; including: chronic conditions; risk factors; and laboratory results as well as classi cations into MI and UA. We evaluate different types of feature selection and apply supervised learning algorithms to a subset of the data. The experimental results are promising; indicating that this type of data could indeed be used to generate accurate models for ACS severity prediction.
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