DocumentCode
1981750
Title
Generating classifier for the acute abdominal pain diagnosis problem
Author
Wozniak, Michal ; Kurzynski, Marek
Author_Institution
Div. of Syst. & Comput. Networks, Wroclaw Univ. of Technol., Poland
Volume
4
fYear
2001
fDate
2001
Firstpage
3819
Abstract
The inductive learning algorithms are very attractive methods for generating hierarchical classifiers. They generate the hypothesis of the target concept on the basis of the set of labeled examples. This paper presents some of the rule generation methods, their usefulness for the rule-base classifier and their quality of classification for the medical decision problem.
Keywords
decision support systems; decision trees; fuzzy logic; learning by example; medical expert systems; pattern classification; acute abdominal pain diagnosis problem; hierarchical classifiers; inductive decision tree algorithms; inductive learning algorithms; labeled examples; medical decision problem; quality of classification; rule generation methods; rule-base classifier; target concept; Abdomen; Algorithm design and analysis; Bellows; Biomedical imaging; Classification tree analysis; Decision support systems; Decision trees; Image databases; Machine learning; Pain;
fLanguage
English
Publisher
ieee
Conference_Titel
Engineering in Medicine and Biology Society, 2001. Proceedings of the 23rd Annual International Conference of the IEEE
ISSN
1094-687X
Print_ISBN
0-7803-7211-5
Type
conf
DOI
10.1109/IEMBS.2001.1019671
Filename
1019671
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