Generating diagnositc rules directly from experimental data

機構典藏 > College of Engineering > Graduate Institute & Department of Electrical Engineering > Journal Article > Item 987654321/60738

Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/60738

Title:	Generating diagnositc rules directly from experimental data
Authors:	Su, Mu-chun;Hsieh, Ching-tang;Chin, Chieh-ching
Contributors:	淡江大學電機工程學系
Keywords:	Neural networks;Fuzzy systems;Computer-aided expert systems;Medical diagnosis
Date:	1997-12
Issue Date:	2013-05-31 11:45:24 (UTC+8)
Publisher:	Singapore: World Scientific Publishing Co. Pte. Ltd.
Abstract:	Traditionally, a major task in building a medical diagnosis expert system is the process of acquiring the required knowledge in the form of production rules. alternative knowledge acquisition approaches to articulating knowledge required for diagnostic tasks are presented in this paper. Each approach has its own advantages and disadvantages. The ultimate goal of these approaches is to free human experts from tedious diagnosis loads. The effectiveness of these approaches is demonstrated by an example of a hypothesis regarding the pathophysiology of diabetes.
Relation:	Biomedical Engineering: Applications, Basis and Communications 9(6), pp.9-14
Appears in Collections:	[Graduate Institute & Department of Electrical Engineering] Journal Article

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