English  |  正體中文  |  简体中文  |  Items with full text/Total items : 2737/2828
Visitors : 340870      Online Users : 8
RC Version 4.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Adv. Search
LoginUploadHelpAboutAdminister

Please use this identifier to cite or link to this item: http://ir.lib.stu.edu.tw:80/ir/handle/310903100/1913

Title: 學習向量量化神經網路拓璞於醫療診斷問題之研究
Learning Vector Quantization Neural Networks to Medical Diagnosis Problems Research
Authors: 潘信利
Pan Hsin-Li
Contributors: 黄建裕
Huang Chien-Yu
資訊管理研究所
Keywords: 醫療診斷;類神經網路;學習向量量化網路;田口方法;直交表
Medical Diagnosis;Artificial Neural Network;Learning Vector Quantization;Taguchi method;Orthogonal Array
Date: 2008
Issue Date: 2011-05-26 11:06:44 (UTC+8)
Publisher: 高雄市:[樹德科技大學資訊管理研究所]
Abstract: 由於目前醫院管理已經逐漸資訊化,醫療資料庫的引用也相當普遍,且資料量的累積可說是與日遽增,相較之下傳統人工方式並不適用於處理如此大量的資料,另外在於傳統的醫學診斷過程中,大多數的醫療診斷問題也只能依靠醫師以往經驗來作出診斷結果。由於現今疾病因素的多元化,如果能配合類神經網路的預測分類技術,則可以大量提高醫療診斷的正確率。本論文將針對醫療相關資料庫進行分類問題之研究,未來可應用於診斷的問題上,並可從龐大的醫療資料庫中,運用學習向量量化網路(Learning Vector Quantization, LVQ)來建立預測分類器,且於前置作業中選擇有意義的屬性,再進一步以田口實驗設計(Taguchi Experimental Design Method, TEDM)的方式來調整學習向量量化網路核心參數,以得到較佳的分類正確率和運算效率,因此可以利用學習向量量化網路來輔助醫師診斷,來達到及早發現及早治療的目的。然而以往傳統的方式在於選擇與設計參數組合時,常需依靠有經驗的技術人員,或利用試誤法(Try and Error)的方式找出最佳參數組合,這通常需耗費大量的時間與成本,因必須反覆的進行實驗測試,可以想像於實驗階段中所耗費的時間及成本。所以本研究提出以學習向量量化網路建立分類預測器,並應用田口實驗設計的方式,找出類神經應用於醫療型資料庫之最佳參數組合。實驗結果顯示在於多種疾病分類上正確率皆逹到九成以上,並得知分類的效果優於其它分類系統。本研究所提出的最佳參數組合,能客觀的應用於多種疾病分類上,並有效逹到不同品質特性的要求,也能有效的縮減實驗中,重複測試的次數,使得在應用上更有效率。
Hospital information management has been computerized gradually, and the medical databases are now quite popular in contrast with traditional storage methods. The traditional manual method is not applicable for a large number of information processing. Moreover, medical diagnosis can only rely on past experience of physicians and there are many diversified factors of disease. In this research, the aim is to provide forecast and classification technology by using Artificial Neural Network in order to support the doctors to improve diagnosis with high accuracy. In accordance with this aim, the research method is to address data set of Medical network to classification issues by using Learning Vector Quantization (LVQ) to establish the prediction and classification parameters as well as pre-operating it to choose the meaningful attributes. Furthermore, Taguchi Experimental Design Method (TEDM) approach is also used to adjust the LVQ core parameters in order to obtain the better classification rate and efficient computing processes. This method applied for Medical database with TEDM to find out the ideal parameter portfolio. The experimental results show that, a variety of disease classification, the accurate rate is more than 90 per cent. Moreover, the ideal parameters portfolio found by using TEDM can reduce the number of repeated testing experiment as well as efficiently applied to other disease classification.
Appears in Collections:[資訊管理系(所)] 博碩士論文

Files in This Item:

File Description SizeFormat
學習向量量化神經網路拓璞於醫療診斷問題之研究__臺灣博碩士論文知識加值系統.htm國圖121KbHTML614View/Open


All items in STUAIR are protected by copyright, with all rights reserved.

 


無標題文件

著作權政策宣告:

1.

本網站之數位內容為樹德科技大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,惟仍請適度,合理使用本網站之內容,以尊重著作權人之權益。商業上之利用,則請先取得著作權人之授權。
 
2. 本網站之製作,已盡力防止侵害著作權人之權益,如仍發現本網站之數位內容有侵害著作權人權益情事者,請權利人通知本校護人員(clairhsu@stu.edu.tw),維護人員將立即採取移除該數位著作等補救措施。
 
DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback