Generation of Diagnostic Rules via Inductive Machine Learning

Generation of Diagnostic Rules via Inductive Machine Learning A learning algorithm called CLILP2 Cover Learning Using Integer Linear Programming is applied to medical data to generate rules to recognize patients with coronary artery disease. The algorithm partitions a data set into subsets using features which best describe and distinguish a particular subset from all other subsets. These features are used to form the rules which can be used as the knowledge base of a diagnostic expert system. Results from the application of the algorithm to coronary artery stenosis data are compared with the results obtained from the existing expert system. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Kybernetes Emerald Publishing

Generation of Diagnostic Rules via Inductive Machine Learning

Kybernetes, Volume 22 (5): 13 – May 1, 1993

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Publisher
Emerald Publishing
Copyright
Copyright © Emerald Group Publishing Limited
ISSN
0368-492X
DOI
10.1108/eb005985
Publisher site
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Abstract

A learning algorithm called CLILP2 Cover Learning Using Integer Linear Programming is applied to medical data to generate rules to recognize patients with coronary artery disease. The algorithm partitions a data set into subsets using features which best describe and distinguish a particular subset from all other subsets. These features are used to form the rules which can be used as the knowledge base of a diagnostic expert system. Results from the application of the algorithm to coronary artery stenosis data are compared with the results obtained from the existing expert system.

Journal

KybernetesEmerald Publishing

Published: May 1, 1993

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