博碩士論文 etd-0803119-053904 詳細資訊


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姓名 塗景盛(Ching-Sheng Tu) 電子郵件信箱 E-mail 資料不公開
畢業系所 電機工程學系研究所(Department of Electrical Engineering)
畢業學位 碩士(Master) 畢業時期 107學年第2學期
論文名稱(中) 機器學習應用於阿茲海默症之分類研究
論文名稱(英) Applying Machine Learning on Classification of Alzheimer's Disease
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    紙本論文:5 年後公開 (2024-09-03 公開)

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    摘要(中) 阿茲海默症的病症發展已經席捲全球逐漸影響全人們,而近年研究發現,不僅會導致記憶衰退,也可能會降低其他行為認知功能。現今大多數醫師使用量表和臨床數據來診斷病人是否患病。但是診斷的過程涉及到患者的配合度與醫師的主觀意識,因此對於病變的診斷產生偏差。本論文根據臨床數據和fMRI數據,將建置一個輔助醫師的自動辨識阿茲海默症系統,阿茲海默症數據來自於高雄醫學大學(KMU)的研究團隊。關於此系統的技術,主要分為三個階段:(1)數據前處理、(2)特徵擷取(相互信息、皮爾森相關係數、線性判別分析)、(3)分類模型(學習矢量量化、支持向量機),並通過實驗結果選擇最佳組合,成為阿茲海默症辨識系統。目前我們在臨床資料的分類上,已經達到不錯的準確度,我們還會探討資料做完共變量修正後的結果是否有助於診斷,並且透過特徵篩選的方法為醫生找出重要的特徵因子,藉此達到輔助醫師判斷阿茲海默症之目標。
    摘要(英) Alzheimer’s disease has gradually affected human beings around the world. It not only causes the decay of memory, but may also decreases other cognitive functions. At present, most doctors use the and outpatient data to determine whether a patient has a disease. Because the judgment process involves the patient’s response and the doctor’s subjective decision, the resulting diagnosis can be questionable. This study intends to establish an automatic identification system which can help doctors to determine the Alzheimer’s disease based on the clinical and fMRI information of the patient from Kaohsiung Medical University (KMU). About the technology of the system, we will divide it into three-stage data pre-processing, feature extraction(Mutual Information, Pearson, Linear Discriminant Analysis), classification model(Learning Vector Quantization, Support Vector Machine), and select the best combination through the experimental results for Clinical Dementia Rating to become our system. We will also explore whether the results of Covariate correction for age, education affect the doctor’s judgment and provide doctors with more important features for patients.
    關鍵字(中)
  • 阿茲海默症
  • 相互資訊
  • 皮爾森相關係數
  • 線性判別分析
  • 學習式向量量化
  • 支撐向量機
  • 臨床失智量表
  • 關鍵字(英)
  • Alzheimer’s Disease
  • Mutual Information
  • Pearson
  • Linear Discriminant Analysis
  • Learning Vector Quantization
  • Support Vector Machine
  • Clinical Dementia Rating
  • 論文目次 論文審定書 i
    誌謝 ii
    摘 要 iii
    Abstract iv
    圖目錄 vii
    表目錄 viii
    第一章 導論 1
    1.1. 研究背景與目的 1
    1.2. 阿茲海默症介紹 2
    1.3. 論文架構 5
    第二章 文獻探討 7
    2.1. 學習向量量化(Learning Vector Quantization, LVQ) 7
    2.2. 支持向量機(Support Vector Machine, SVM) 8
    第三章 研究方法 10
    3.1. 利用MI、Pearson與LDA前處理 11
    3.2. 將特徵篩選與合併的數據運用分類器進行分類比較其結果 13
    3.3. 阿茲海默症患者的臨床資料進行前處理。 14
    3.4. 因為數據的原始性,我們將運用KMEANS進行濾除雜訊 15
    3.5. 分類出阿茲海默症病患與指標介紹 16
    第四章 實驗結果 17
    4.1. 特徵選取與縮減的實驗 19
    4.2. 二階特徵選取與縮減的實驗 22
    4.3. 臨床資料的實驗測試 23
    4.4. 臨床資料濾除雜訊的實驗測試 23
    4.5. 前處理臨床資料實驗測試以及維度排序 24
    4.6. 與其他方法比較 25
    第五章 結論與未來展望 26
    5.1. 結論 26
    5.2. 未來研究方向 26
    參考文獻 27
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    口試委員
  • 侯俊良 - 召集委員
  • 劉志峰 - 委員
  • 林展霈 - 委員
  • 歐陽振森 - 委員
  • 李錫智 - 指導教授
  • 口試日期 2019-07-29 繳交日期 2019-09-03

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