INDUCTIVE AND BAYESIAN LEARNING IN MEDICAL DIAGNOSIS

被引:208
作者
KONONENKO, I [1 ]
机构
[1] UNIV LJUBLJANA,FAC ELECT & COMP ENGN,LJUBLJANA 61001,SLOVENIA
关键词
Bayesian learning - Inductive learning - Medical diagnosis;
D O I
10.1080/08839519308949993
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
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.
引用
收藏
页码:317 / 337
页数:21
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