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研究生:黃上益
研究生(外文):Shang-Yi Huang
論文名稱:運用資料探勘技術於動脈粥狀硬化預測模式之研究
論文名稱(外文):Applying Data Mining to Construct Predictive Models of Atherosclerosis
指導教授:鄭博文鄭博文引用關係
指導教授(外文):Bor-Wen Cheng
學位類別:碩士
校院名稱:國立雲林科技大學
系所名稱:工業工程與管理研究所碩士班
學門:工程學門
學類:工業工程學類
論文種類:學術論文
畢業學年度:95
語文別:中文
論文頁數:76
中文關鍵詞:腦血管疾病類神經網路心血管疾病動脈粥狀硬化C5.0決策樹
外文關鍵詞:AtherosclerosisCardiovascular DiseasesC5.0 Decision TreeNeural Networks
相關次數:
  • 被引用被引用:33
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  • 下載下載:175
  • 收藏至我的研究室書目清單書目收藏:1
根據行政院衛生署調查國人至民國九十四年十大死亡原因統計,發現腦血管疾病和心血管疾病分別名列國人死亡主因的二、三名。動脈粥狀硬化是一種慢性動脈病變疾病,與心腦血管疾病發生有很大的關聯。由於血管中的內膜增厚,造成血管狹窄,再加上血液中,小血塊間接形成,使血管堵塞,是心腦血管疾病發生的主要原因。而因血管粥狀硬化造成身體局部中風,讓許多家庭在患者的醫療費用及照顧上更需花費不少心力及醫療支出。
本研究資料來源是以中部某教學醫院健康檢查資料來做研究對象,研究分成兩個階段執行,第一階段比較C5.0決策樹及類神經網路兩種模式的預測績效,建構最佳預測模型。研究結果,C5.0決策樹的預測模型正確率優於類神經網路模型。第二階段是將不同方式篩選的因子投入C5.0決策樹中分別建構出預測模型,評選出預測正確率高且對於醫療臨床有參考性的模型為本研究最適模型。研究結果為以多項羅吉司迴歸篩選出的七項因子所建立出的C5.0決策樹模型較符合醫學臨床所需,為本研究最適模型(訓練正確率83.07%,測試正確率74.11%,規則共五十條)。此外,本研究發現胰澱粉脢及r-GT(肝功能)兩個因子對於產生動脈粥狀硬化具有高度相關性,是現今醫學在動脈粥狀硬化症研究上所少有探討的。
Based on the statistics of the Department of Health, Executive Yuan in Taiwan, stroke and cardiovascular diseases (CVD) are the second and third major causes of death, respectively. Atherosclerosis is a chronic disease of arteries and has a strong association with both stroke and CVD. Blood vessels narrowed by increase in intimae thickness and clotting of small blood plaques are major causes of blood vessel blockage. Due to atherosclerosis-caused ischemia stroke, many families need to spend greater emotional and medical expenditure to take care of atherosclerotics.
The subjects of the study were from the database of health examination in a teaching hospital in middle Taiwan. There were two steps to the study. First, comparison of the accuracy between C5.0 Decision Tree (C5.0 DT) and Neural Networks (NN). The result shows that C5.0 DT is better than NN in terms of accuracy. Second, the potentiality factors were sieved out from different methods and were put into C5.0 DT. Finally, the best predictive model was chosen for the references of clinical treatment. The results show that factors sieved out by multinomial logistic regression produces the best performance (predictive accuracy for training is 83.07%, predictive accuracy for testing is 74.11% and total for fifty decision rules). Furthermore, the results also found two factors, amylase and r-GT (liver function), are highly associated with atherosclerosis, a finding that has not been discussed in the past.
目錄

中文摘要 i
英文摘要 ii
誌謝 iv
目錄 v
表目錄 vii
圖目錄 viii
第一章 緒論 1
1.1研究背景與動機 1
1.2研究目的 4
1.3 研究範圍與限制 4
1.4 研究架構 5
第二章 文獻探討 6
2.1 動脈粥狀硬化 6
2.2 心腦血管疾病 8
2.3 潛在危險性因子探討 10
2.4 資料探勘 15
2.4.1 決策樹 15
2.4.2 類神經網路 17
2.5 資料探勘技術於醫學上的研究 19
第三章 研究方法 20
3.1 研究架構 20
3.2 資料收集及前置處理 23
3.2.1 資料屬性 23
3.2.2 資料前置處理 25
3.2.3 變數篩選 26
3.3 模型建構 27
3.3.1 K疊交互驗證法 27
3.3.2 決策樹模型建立流程 29
3.3.3 類神經網路模型建立流程 30
3.4 模型評估 34
第四章 研究結果分析 36
4.1 訓練樣本集與測試樣本集資料量的選定 36
4.1.1訓練樣本集與測試樣本集資料量的選定-C5.0決策樹 37
4.1.2 訓練樣本集與測試樣本集資料量的選定-類神經網路 39
4.2 C5.0決策樹模型建構 40
4.3類神經網路模型建構 42
4.3.1 最佳參數組合 42
4.3.2預測模型建構 45
4.4 模型比較 47
4.4.1 C5.0決策樹與類神經網路模型比較 47
4.4.2 篩選因子對預測模型之績效評估 48
4.4.3 最佳預測模型選定 50
第五章 結論與建議 55
5.1 研究發現及討論 55
5.2 未來研究建議 57
5.3 結論與貢獻 58
參考文獻 59
附錄一 64
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