Generation of Diagnostic Rules via Inductive Machine Learning

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.

Citation

Cios, K.J., Liu, N. and Goodenday, L.S. (1993), "Generation of Diagnostic Rules via Inductive Machine Learning", Kybernetes, Vol. 22 No. 5, pp. 44-56. https://doi.org/10.1108/eb005985