Classifying the Severity of an Acute Coronary Syndrome by Mining Patient Data

Document type: Conference Papers
Peer reviewed: Yes
Author(s): Niklas Lavesson, Anders Halling, Michael Freitag, Jacob Odeberg, Håkan Odeberg, Paul Davidsson
Title: Classifying the Severity of an Acute Coronary Syndrome by Mining Patient Data
Conference name: 25th Annual Workshop of the Swedish Artificial Intelligence Society
Year: 2009
Pagination: 55-63
Publisher: Linköping University Electronic Press
City: Linköping
Other identifiers: ISSN 1650-3686
Organization: Blekinge Institute of Technology
Department: School of Engineering - Dept. of Systems and Software Engineering (Sektionen för teknik – avd. för programvarusystem)
School of Engineering S- 372 25 Ronneby
+46 455 38 50 00
Authors e-mail:
Language: English
Abstract: 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 different 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 classifications 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.
Subject: Computer Science\Artificial Intelligence
Medical Sciences
Keywords: acute coronary syndrome, acs, myocardial infarction, unstable angina, diagnosis, severity, data mining, classification