Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/92954
標題: Application of Dementia Screening Classification System Based on Over-Sampling Approach and Decision Tree
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
作者: 郭丞軒
Cheng-Hsuan Kuo
關鍵字: Dementia
Data mining
Decision tree
Statistica
CHAID
失智症
資料探勘
決策樹
Statistica
CHAID
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摘要: The 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.
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。
URI: http://hdl.handle.net/11455/92954
其他識別: U0005-1707201512183300
文章公開時間: 2018-07-23
Appears in Collections:資訊管理學系

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