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Probabilistic classification of acute myocardial infarction from multiple cardiac markers

Wilson, P.C., Irwin, G.W., Lamont, J.V. and Harrison, R.F. (2008) Probabilistic classification of acute myocardial infarction from multiple cardiac markers. Pattern Analysis & Applications, 12 (4). pp. 321-333. ISSN 1433 - 755X

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Abstract

Logistic regression and Gaussian mixture model (GMM) classifiers have been trained to estimate the probability of acute myocardial infarction (AMI) in patients based upon the concentrations of a panel of cardiac markers. The panel consists of two new markers, fatty acid binding protein (FABP) and glycogen phosphorylase BB (GPBB), in addition to the traditional cardiac troponin I (cTnI), creatine kinase MB (CKMB) and myoglobin. The effect of using principal component analysis (PCA) and Fisher discriminant analysis (FDA) to preprocess the marker concentrations was also investigated. The need for classifiers to give an accurate estimate of the probability of AMI is argued and three categories of performance measure are described, namely discriminatory ability, sharpness, and reliability. Numerical performance measures for each category are given and applied. The optimum classifier, based solely upon the samples take on admission, was the logistic regression classifier using FDA preprocessing. This gave an accuracy of 0.85 (95% confidence interval: 0.78–0.91) and a normalised Brier score of 0.89. When samples at both admission and a further time, 1–6 h later, were included, the performance increased significantly, showing that logistic regression classifiers can indeed use the information from the five cardiac markers to accurately and reliably estimate the probability AMI.

Item Type: Article
Copyright, Publisher and Additional Information: © 2008 Springer. This is an author produced version of a paper published in Pattern Analysis and Applications. Uploaded in accordance with the publisher's self archiving policy.
Keywords: acute myocardial infarction, AMI, cardiac markers, diagnostic aid, probabilistic classification
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield)
Depositing User: Sherpa Assistant
Date Deposited: 31 Jul 2008 10:07
Last Modified: 08 Feb 2013 16:56
Published Version: http://dx.doi.org/10.1007/s10044-008-0126-x
Status: Published
Publisher: Springer
Refereed: Yes
Identification Number: 10.1007/s10044-008-0126-x
URI: http://eprints.whiterose.ac.uk/id/eprint/4125

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