Generating Diagnostic Rules Directly from Experimental Data

機構典藏 > College of Engineering > Graduate Institute & Department of Electrical Engineering > Proceeding > Item 987654321/96021

Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/96021

Title:	Generating Diagnostic Rules Directly from Experimental Data
Authors:	Su, Mu-Chun
Contributors:	淡江大學電機工程學系
Keywords:	診斷;實驗數據;類神經網路;模糊系統;Diagnosis;Experimental Data;Neural Network;Fuzzy System
Date:	1996-05
Issue Date:	2014-02-13 11:35:44 (UTC+8)
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(IF...THEN...). Alternative knowledge acquisition approach 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:	第二屆國際醫學工程週論文集=Proceedings of the 2nd Medical Engineering Week of the World，頁438-443
Appears in Collections:	[Graduate Institute & Department of Electrical Engineering] Proceeding

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