Comparative review of knowledge engineering and inductive learning using data in a medical domain

Nicolino J. Pizzi; Dick Jackson

doi:10.1117/12.21116

1 January 1990 Comparative review of knowledge engineering and inductive learning using data in a medical domain

Nicolino J. Pizzi, Dick Jackson

Proceedings Volume 1293, Applications of Artificial Intelligence VIII; (1990) https://doi.org/10.1117/12.21116
Event: 1990 Technical Symposium on Optics, Electro-Optics, and Sensors, 1990, Orlando, FL, United States

Abstract

This paper analyzes two different methodologies that were employed in the classification of a set of tonsillectomy/adenoidectomy patients into normal and abnormal diagnostic groups with respect to their predispositions to bleeding. An expert system was designed using conventional knowledge engineering strategies and an inductive learning technique was used to infer generalizations directly from the patient data. The former approach involved the development of a hematology expert system which serves as a preoperative filter to identify high risk patients. The system assesses bleeding tendencies as may occur with patients with certain hemostatic conditions such as platelet function defect, von Willebrand’s disease, and hemophilia. The specific inductive classification methodology used in this review was Quinlan’s ID3 induction algorithm which searches for underlying regularities in data sets. Quinlan’s method for inducing generalizations from a training set of events involves the creation of a classification tree whose root and intermediate nodes are decision points based upon the entropy measurements of the events’ attributes at those decision points. The generated leaf nodes correspond to the classification outcomes. This paper discusses the respective performances of the expert system and inductive learning methodologies including sensitivity and specificity measurements, reveals some of the problems encountered in the use of the two techniques, and suggests some possible remedies. The method used for the selection of the patient parameters for use by the ID3 algorithm is also discussed. The classification rules from both techniques are discussed and some computational and clinical (hematological) justifications are proposed concerning the disparity of the rule sets. Finally, some reflections are offered concerning the potential amalgamation of knowledge engineering strategies and inductive learning techniques

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Nicolino J. Pizzi and Dick Jackson "Comparative review of knowledge engineering and inductive learning using data in a medical domain", Proc. SPIE 1293, Applications of Artificial Intelligence VIII, (1 January 1990); https://doi.org/10.1117/12.21116

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