INDUCTIVE AND BAYESIAN LEARNING IN MEDICAL DIAGNOSIS
作者:
IGOR KONONENKO,
期刊:
Applied Artificial Intelligence
(Taylor Available online 1993)
卷期:
Volume 7,
issue 4
页码: 317-337
ISSN:0883-9514
年代: 1993
DOI:10.1080/08839519308949993
出版商: Taylor & Francis Group
数据来源: Taylor
摘要:
Although successful in medical diagnostic problems, inductive learning systems were not widely accepted in medical practice. In this paper two different approaches to machine learning in medical applications are compared: the system for inductive learning of decision trees Assistant, and the naive Bayesian classifier. Both methodologies were tested in four medical diagnostic problems: localization of primary tumor, prognostics of recurrence of breast cancer, diagnosis of thyroid diseases, and rheumatology. The accuracy of automatically acquired diagnostic knowledge from stored data records is compared, and the interpretation of the knowledge and the explanation ability of the classification process of each system is discussed. Surprisingly, the naive Bayesian classifier is superior to Assistant in classification accuracy and explanation ability, while the interpretation of the acquired knowledge seems to be equally valuable. In addition, two extensions to naive Bayesian classifier are briefly described: dealing with continuous attributes, and discovering the dependencies among attributes.
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