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姓名 游英杰(Ying-Jie You) 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 108學年第2學期
論文名稱(中) 機器學習應用於臨床診斷阿茲海默症之分類研究
論文名稱(英) Applying Machine Learning to the Classification in Clinical Diagnosis of Alzheimer's Disease
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    紙本論文:5 年後公開 (2025-09-01 公開)

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    摘要(中) 2019年國際失智症協會(ADI)預估全球有超過5千萬名的失智者,可以說失智症在未來對人們影響深遠,而在失智症中又以阿茲海默症為多數,它不僅會導致記憶力衰退,還會降低其他認知功能。當前醫生大多使用患者臨床數據、腦部影像與認知測驗等來確認患者是否患有此症。但由於目前阿茲海默症的成因至今仍然未明確,患者須做各方面的測試與檢查。這項研究目的在於透過機器學習建立一個自動識別系統,該系統會根據高雄醫學大學(KMU)病人的臨床數據進行相關細分析後確定阿茲海默症的程度,達到減少時間與成本的目的。關於該系統的技術,我們將其分為資料前處理、特徵相關性分析、分類模型等階段,針對臨床癡呆分級的選擇適當的技術成為我們的系統並輔助醫師進行阿茲海默症的診斷。
    摘要(英) In 2019, the International Dementia Association (ADI) estimates that there are more than 50 million dementia people in the world. It can be said that dementia will have a profound impact on people in the future. Alzheimer's disease is the majority in dementia, which not only causes memory loss, but also reduces other cognitive functions. At present, doctors mostly use patient clinical data, brain imaging and cognitive tests to confirm whether patients have this disease. However, because the cause of Alzheimer's disease is still unclear, patients must do various tests and examinations. The purpose of this research is to establish an automatic recognition system through machine learning. The system will determine the degree of Alzheimer's disease after detailed analysis based on the clinical data of Kaohsiung Medical University (KMU) patients to reduce the time and cost. Regarding the technology of the system, we divide it into the stages of data pre-processing, feature correlation analysis, classification model, etc. Choosing appropriate technology for clinical dementia grading becomes our system and assists doctors in the diagnosis of Alzheimer's disease.
    關鍵字(中)
  • 臨床數據
  • 分類
  • 機器學習
  • 特徵相關性分析
  • 阿茲海默症
  • 關鍵字(英)
  • Machine Learning
  • Classification
  • Alzheimer’s Disease
  • Clinical data
  • feature correlation analysis
  • 論文目次 目錄
    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 1
    1.2. 研究動機與目的 2
    1.3. 阿茲海默症介紹 4
    1.4. 論文架構 7
    1.5. 論文貢獻 7
    第二章 文獻探討 9
    2.1. 特徵篩選(Feature Selection) 9
    2.2. 分類技術(Classifier) 9
    第三章 研究方法 13
    3.1. 阿茲海默症患者的臨床資料進行前處理 14
    3.2. 利用相關性分析進行特徵篩選 16
    3.2.1. 相互資訊(Mutual Information, MI) 16
    3.2.2. Pearson相關係數 17
    3.2.3. 資訊增益(Information Gain, IG) 18
    3.2.4. 遺傳演算法(Genetic Algorithm, GA) 18
    3.2.5. 線性判別分析(Linear Discriminant Analysis, LDA) 19
    3.2.6. 主成分分稀(Principal Component Analysis, PCA) 20
    3.3. 分類技術 21
    3.3.1. SVM 21
    3.3.2. RBF Network 21
    第四章 實驗結果 24
    4.1. 評估指標介紹 24
    4.2. 數據介紹 25
    4.3. 阿茲海默症資料實驗測試 31
    4.3.1. 單一階段特徵篩選應用於阿茲海默症資料之實驗測試 31
    4.3.2. 兩階段特徵篩選應用於阿茲海默症資料之實驗測試 37
    4.3.3. Both與認知測驗/非認知測驗之比較 42
    4.4. 臨床相關檢測項目之相關性分析 50
    第五章 結論與未來展望 55
    5.1. 結論 55
    5.2. 未來研究方向 55
    參考文獻 56
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    口試委員
  • 吳志宏 - 召集委員
  • 侯俊良 - 委員
  • 歐陽振森 - 委員
  • 陳佳如 - 委員
  • 李錫智 - 指導教授
  • 口試日期 2020-07-23 繳交日期 2020-09-01

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