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.
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