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研究生:吳昇翰
研究生(外文):Wu, Sheng-Han
論文名稱:利用集成化分類器建構臨床決策支援系統
論文名稱(外文):Construction of a Clinical Decision Support System Using Ensemble Classification
指導教授:陳永福陳永福引用關係
指導教授(外文):Chen, Yung-Fu
口試委員:林宣宏陳天送
口試委員(外文):Lin, Hsuan-HungChen, Tien-Sung
口試日期:2013-01-18
學位類別:碩士
校院名稱:中臺科技大學
系所名稱:醫學工程暨材料研究所
學門:工程學門
學類:綜合工程學類
論文種類:學術論文
論文出版年:2013
畢業學年度:101
語文別:中文
論文頁數:58
中文關鍵詞:臨床決策支援系統集成化基因演算法支援向量機
外文關鍵詞:Clinical decision support systemEnsemble classifierGenetic algorithmSupport vector machines
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臨床決策支援系統(CDSS)可以協助臨床工作人員在診斷與照護過程中給予支援,提供知識與訊息,提昇工作效率,幫助改善病患的照護品質。因此正確精準的數據分析成為其中關鍵,現今已開發多種數據分類法,並且應用在各領域的大型數據分析,提取有價值的信息。然而,單一分類器並非對所有數據集都可以保持一貫性的良好表現,為了改善其缺點,集成化分類器被提出來改善分類效能。本研究主題以基因演算法(GA)結合支援向量機(SVM)來建構臨床決策支援系統,實驗結果顯示,利用多個分類器集成化的特徵篩選比傳統的分類器有更優異的效能。本研究所提出的方法(EnsCV)和BAIS分類法針對UCI機器學習數據庫中的11組數據之準確率,比較結果顯示EnsCV有較高的準確率,尤其是Sonar與Glass數據集;以Sonar而言,EnsCV的準確率比BAIS高出6.9%,分別為93.3%與86.4%;而數據集Glass的分類結果顯示,EnsCV的準確率比BAIS高出13.3%,分別為86.9%與73.6%。將多項分類器集成化的特徵篩選較傳統的分類器優異,本研究方法預期可以應用在臨床決策支援系統之設計。

Clinical Decision Support System (CDSS) assists clinical staff in the diagnoses of diseases, provision of information for care support, and improvement of efficiency to enhance the quality of care. Accurate data analysis is the crucial element during the diagnosis process. Nowadays novel techniques have been developed to provide a wide range of data analysis and applied in various fields to obtain valuable information. However, single classifier does not perform consistently for all data sets. Integration of recommendations and proposed measures for improving classification effectiveness were suggested to avoid the inconsistency. In this research, Genetic Algorithm (GA) was combined with Support Vector Machine (SVM) to provide a foundation for CDSS design. The proposed EnsCV method was compared with BAIS based on the data in the UCI machine learning database containing 11 datasets. It was shown that EnsCV is more effective, especially for Sonar and Glass, in the classification of categorical datasets. For instance, the accuracy using the proposed EnsCV method is 6.9% and 13.3% higher than the BAIS in classifying the datasets Sonar (EnsCV: 93.3%; BAIS: 86.4%) and Glass (EnsCV: 86.9%; BAIS: 73.6%), respectively. It is concluded that the ensemble classifiers present greater classification performance than the traditional method.

致謝 I
中文摘要 III
Abstract IV
目錄 V
圖目錄 VII
表目錄 VIII
第一章 緒論 1
1-1研究背景與動機 1
1-2研究目的 2
第二章 文獻探討 3
2-1集成化分類器(Ensemble) 3
2-2基因演算法(Genetic Algorithm , GA) 4
2-3支援向量機(Support Vector Machines, SVM) 4
2-4 Integrated GA and SVM,IGS 5
2-5 貝氏人工免疫系統(Bayesian Artificial Immune System,BAIS) 6
2-6臨床決策支援系統(Clinical decision support system,CDSS) 7
2-7 UCI機器學習數據庫(UC Irvine Machine Learning Repository) 9
2-8呼吸器脫離(Ventilation Weaning) 10
2-9阻塞性睡眠呼吸中止(Obstructive Sleep Apnea) 10
2-10子宮頸癌(Cervical Cancer) 12
2-11過去的研究 12
第三章 研究方法 24
3-1 材料 24
3-1-1支援向量機(Support Vector Machines,SVM) 24
3-1-2基因演算法(Genetic Algorithm,GA) 29
3-2 方法 31
3-2-1資料收集 32
3-2-2研究流程 34
3-2-3 分析 36
第四章 結果與討論 39
4-1結果 39
4-1-1第一組實驗 39
4-1-2第二組實驗 40
4-2討論 40
第五章 結論 42
參考文獻 43

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