LEARNING OF FUZZY PRODUCTION RULES FOR MEDICAL DIAGNOSIS
Elsevier eBooksPublished 1 January 1993
Leonardo Lesmo, Lorenza Saitta, Pietro Torasso
Citations21
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TL;DR
An algorithm for the automatic learning of the fuzzy production rules and its application to the domain of liver pathology is outlined and the effectiveness of the learning algorithm is shown.
Abstract
The methods used so far to develop systems for medical consultation relied either on classical probabilistic and pattern recognition schemata or on techniques developed in the field of Artificial Intelligence. The statistical approach guarantees the availability of efficient learning algorithms, but the structure of the decision rules is too far from the methods used by the physicians, thus preventing the designer from inserting good explanation facilities into the system. On the other hand, artificial intelligence systems generally require a noticeable effort to encode the expert's knowledge in a form suitable to perform inferences on the available data. The system described in this paper tries to combine the respective advantages of the mentioned approaches, by using fuzzy production rules as the basic deductive mechanism. The paper outlines an algorithm for the automatic learning of the fuzzy production rules and shows its application to the domain of liver pathology. The effectiveness of the learning algorithm is shown by reporting a set of experimental results which allow to compare the outcome of the learned production rules with the classification available from the experts.
Keywords
Computer Science
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