Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/92954
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dc.contributor蔡孟勳zh_TW
dc.contributor.author郭丞軒zh_TW
dc.contributor.authorCheng-Hsuan Kuoen_US
dc.contributor.other資訊管理學系所zh_TW
dc.date2015zh_TW
dc.date.accessioned2015-12-16T05:55:49Z-
dc.identifierU0005-1707201512183300zh_TW
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dc.identifier.urihttp://hdl.handle.net/11455/92954-
dc.description.abstractThe problem of population aging in Taiwan is getting worse, the diseases of elderly patients are considered to be an important issue. Dementia, one of brain diseases for elderly people, is a degeneration of brain diseases. People with dementia have significantly impaired intellectual functioning that interferes with normal activities and relationships. Eventually, patients will complete loss of mental functions. This serious problem is not only affected the normal daily life but also making great burden to society. Therefore, diagnosis and treatment of dementia will be an important issue for medical research. The aims of this study were to develop a Dementia Screening Classification System to differentiate dementia patients and to study the relevant variables in diagnosis. The type of dementia which elderly patients suffered would be classified through this system. The original data was obtained from medical center based on the Cognitive Abilities Screening Instrument (CASI) test. Data discretization and Synthetic minority over-sampling technique were used to pre-processing this data. Then, Classification and Regression Tree (CART) and Chi-Square Automatic Interaction Detector (CHAID) tree-based algorithms were used to classify this data. As the result of decision tree, age is an important factor which affects to brain diseases for elderly people. Finally, a Dementia Screening Classification System was constructed. Unified Theory of Acceptance and Use of Technology (UTAUT) model and Theory of Planned Behavior (TPB) model were adopted to check the reliability of the system. Results revealed a good model fit and of the seven hypotheses formulated in this study, five were supported.en_US
dc.description.abstract台灣人口老化問題逐年增加,因此高齡患者疾病也日益受到重視,失智症即為大部分年長者容易罹患的腦部疾病之一,失智症為一種腦部功能不正常退化疾病現象,主要的病徵為記憶能力和部分認知功能的衰敗與退化,最終使得病患心智功能完全喪失,甚至死亡,不但嚴重影響患者的正常生活作息,同時也將會造成親友及社會莫大的負擔,隨著醫療科技進步,人口結構持續老化,失智症盛行率也不斷增加,因此,失智症的診斷與治療將是醫學及社會上值得重視的議題。 本研究旨在建立一套失智症患者的篩檢模型,年長者可在監護人或是醫療人員的陪同下進行失智症篩檢並可透過此模型分類結果得知罹患何種類型失智症,本研究資料為醫療院所病患進行認知功能障礙篩檢量表(CASI)所得測驗結果之各項領域評分配合病患資訊作為原始資料,進行資料預處理,將細項的疾病類型歸納合併,部分資料屬性進行離散化處理,使用增量技術(SMOTE)調節不平衡資料,接著以分類回歸樹與卡方自動交互檢示法作為資料探勘決策樹方法,經分類結果顯示以年齡作為決策樹第一分類節點,由此可知年齡為影響老年疾病的重要屬性,最後利用決策樹診斷結果作為分類模型演算法,建立失智症篩檢分類系統,該系統透過整合科技接受模型(UTAUT)與計畫行為理論(TPB)所整合之理論模型所設定前測問卷檢視使用者意願,計算信度、皮爾森相關分析與路徑分析,結果顯示七項所設立的假說中,其中五項呈現顯著結果。zh_TW
dc.description.tableofcontents誌謝 i 目錄 ii 圖目錄 v 表目錄 vii 中文摘要 viii 英文摘要 ix 第 一 章 緒論 1 1.1 研究背景 1 1.2 研究動機與目的 2 1.3 研究論文架構 3 第 二 章 文獻探討 4 2.1 失智症介紹 4 2.1.1 何謂失智症 4 2.1.2 失智症分類 4 2.1.3 台灣早期失智症研究調查 5 2.2 資料探勘進行醫療診斷 5 2.3 資料不平衡 7 2.3.1 何謂資料不平衡 7 2.3.2 改善資料不平衡方法 8 2.4 認知功能篩檢測驗CASI 9 2.5 理論模型 11 2.5.1 整合科技接受模型 11 2.5.2 計畫行為理論 14 第 三 章 研究方法 18 3.1 資料說明 18 3.1.1 資料來源與描述 18 3.2 資料預處理 20 3.2.1 資料整理 20 3.2.2 屬性整理 20 3.2.3 增量技術 23 3.2.4 資料離散化 24 3.3 資料探勘 25 3.3.1 分類回歸樹-CART 25 3.3.2 卡方自動交互檢視法-CHAID 26 3.3.3 決策樹演算法之比較 27 3.4 研究問卷編制 28 3.4.1 研究問卷設計 28 3.4.2 研究問卷假說 28 3.4.3 問卷實施 30 第 四 章 研究結果與討論 32 4.1 資料預處理結果 32 4.1.1 資料整理與SMOTE 32 4.1.2 資料屬性介紹 34 4.2 決策樹建構成果 36 4.2.1 分類回歸樹-CART 36 4.2.2 卡方自動交互檢視法-CHAID 39 4.2.3 決策樹分析結果討論 41 4.3 問卷結果分析 42 4.3.1 問卷資料來源 42 4.3.2 信度分析 47 4.3.3 皮爾森相關係數分析 48 4.3.4 路徑分析結果討論 49 4.4 系統實作與展示 50 4.4.1 系統設計理念 50 4.4.2 平台與環境 51 4.4.3 系統功能介紹 51 4.4.4 系統流程圖 57 第 五 章 結論與未來展望 58 參考文獻 59 附錄 62zh_TW
dc.language.isozh_TWzh_TW
dc.rights同意授權瀏覽/列印電子全文服務,2018-07-23起公開。zh_TW
dc.subjectDementiaen_US
dc.subjectData miningen_US
dc.subjectDecision treeen_US
dc.subjectStatisticaen_US
dc.subjectCHAIDen_US
dc.subject失智症zh_TW
dc.subject資料探勘zh_TW
dc.subject決策樹zh_TW
dc.subjectStatisticazh_TW
dc.subjectCHAIDzh_TW
dc.titleApplication of Dementia Screening Classification System Based on Over-Sampling Approach and Decision Treeen_US
dc.title植基於增量技術與決策樹分類系統在失智症篩檢之研究與應用zh_TW
dc.typeThesis and Dissertationen_US
dc.date.paperformatopenaccess2018-07-23zh_TW
dc.date.openaccess2018-07-23-
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