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研究生:邱毅平
研究生(外文):CHIU, YI-PING
論文名稱:運用資料探勘技術建構攝護腺術式預 測模型
論文名稱(外文):Apply data mining technique for the prostate surgery predictive model construction
指導教授:胡雅涵胡雅涵引用關係、李珮如李珮如引用關係
指導教授(外文):HU, YA-HAN、LEE, PEI-JU
口試委員:蔡志豐、王明昌、胡雅涵、李珮如
口試委員(外文):TSAI, CHIH-FONG、WANG, MING-CHANG、HU, YA-HAN、LEE, PEI-JU
口試日期:2022-08-30
學位類別:碩士
校院名稱:國立中正大學
系所名稱:資訊管理學系碩士在職專班
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2022
畢業學年度:110
語文別:中文
論文頁數:58
中文關鍵詞:資料探勘、預測模型、攝護腺、下泌尿道排尿症狀、雷射、電刀、手術
外文關鍵詞:Data mining、predictive model、prostate、lower urinary tract symptom、laser、electrosurgery、surgery
相關次數:
  • 被引用被引用:1
  • 點閱點閱:333
  • 評分評分:
  • 下載下載:69
  • 收藏至我的研究室書目清單書目收藏:1
英國的研究發現,年齡大於 85 歲時,下泌尿道症狀的發生率超過 30%。下泌尿
道症狀包含頻繁解尿,急迫性解尿,夜間解尿,延遲性解尿,間斷性解尿,解尿時
腹部用力,解尿時間過長,排尿時解不乾淨,解尿結束後出現滴尿。攝護腺肥大在
口服藥物治療無效及其他特定狀況出現,手術治療會優先於藥物治療。我們藉由本
研究、運用資料探勘技術來尋找出攝護腺術式選擇的影響因子並建構及選擇出良好
預測效能的模型。
本試驗使用某醫院病歷資訊系統,收集 2012 年 6 月到 2013 年 12 月接受攝護腺
手術治療病患的資料。收案數量共 391 位,接受雷射術式數量為 268 位,接受傳統
電刀術式為 123 位,使用 WEKA 3.8.1 程式進行資料探勘處理及預測模型的建構。
試驗結果中,表現優秀的預測模型為 J48、LWL 及 DecisionTable 這三種。在訓
練組時的 ROC area 項目的優劣排序依次為 J48( 0.8227),DecisionTable
( 0.8111),LWL( 0.761)。在測試組時,ROC area 項目的優劣排序依次為
J48( 0.8565),DecisionTable ( 0.8384),LWL( 0.8133)。本試驗能為這類需要面
臨手術選擇的醫師或病患,提供試驗得知的資訊。J48、LWL 及 DecisionTable 這三
種預測模型在訓練組和測試組的結果都很優秀。
The study from British found that the incidence of lower urinary tract symptoms is more than 30% in people older than 85 years. Lower urinary tract symptoms include frequency, urgency, nocturia, delayed urination, intermittency, abdominal strain, prolonged urination, incomplete bladder empty, and terminal dribbling. If prostate hyperplasia treatment outcome with medicine is poor, surgical intervention should be considered.The purpose of this study is to find out which factor will influence the surgery method by using data mining techniques to construct models with good predictive performance.
We use the medical record information system from a medical center, patients who
received prostate surgery from June 2012 to December 2013 were collected. Total of 391 cases were received, 268 received laser method, and 123 received traditional electrosurgery. The WEKA 3.8.1 program was used for data mining processing and prediction model construction.
The three excellent prediction models are J48, LWL and DecisionTable. The ranking of the ROC area items in the training group was J48 ( 0.8227), DecisionTable ( 0.8111), and LWL ( 0.761). In the test group, the ranking of the ROC area items was J48 ( 0.8565), DecisionTable ( 0.8384), and LWL ( 0.8133).This trial can provide information to physicians and patients who will choose method of surgical intervention. The three prediction models, J48, LWL and DecisionTable, have excellent results in both training and testing groups.
第一章 緒論 ..........................................................................................1
1.1 研究背景 ................................................................................................................. 1
1.2 研究動機 ................................................................................................................. 4
1.3 研究目的 ................................................................................................................. 6
1.4 研究流程 ................................................................................................................. 7
第二章 文獻探討................................................................................................................. 8
2.1 使用抗凝血劑對術式選擇的影響 ......................................................................... 8
2.2 年齡對術式選擇的影響 ....................................................................................... 13
2.3 攝護腺體積對術式選擇的影響 ........................................................................... 14
第三章 研究方法............................................................................................................... 18
3.1 資料來源 ............................................................................................................... 18
3.2 資料前處理 ........................................................................................................... 22
3.3 資料探勘技術 ....................................................................................................... 23
3.3.1 決策樹........................................................................................................ 23
3.3.2 多層感知器................................................................................................ 24
3.3.3 序列最小優化演算法................................................................................ 24
3.3.4 隨機梯度下降( SGD) ................................................................................ 24
3.3.5 Ibk............................................................................................................... 24
3.3.6 LWL............................................................................................................ 25
3.3.7 引導聚集算法( Bagging)........................................................................... 25
3.3.8 貝氏分類器................................................................................................ 26
3.3.9 決策樹樁( DecisionStump)........................................................................ 26
3.3.10 決策表( Decision table) ........................................................................... 26

VII

第四章 資料研究評估結果與分析................................................................................... 27
4.1 資料探勘分析過程 ............................................................................................... 27
4.2 資料探勘結果 ....................................................................................................... 22
4.2.1 J48 設定資料及試驗運作結果.................................................................. 23
4.2.2 MultiplelayerPerceptron 設定資料及試驗運作結果 ................................ 30
4.2.3 SMO 設定資料及試驗運作結果............................................................... 31
4.2.4 NaiveBayes 設定資料及試驗運作結果.................................................... 32
4.2.5 SGD 設定資料及試驗運作結果 ............................................................... 33
4.2.6 IBk 設定資料及試驗運作結果 ................................................................. 34
4.2.7 LWL 設定資料及試驗運作結果............................................................... 35
4.2.8 Bagging 設定資料及試驗運作結果.......................................................... 37
4.2.9 DecisionTable 設定資料及試驗運作結果 ................................................ 38
4.2.10 DecisionStump 設定資料及試驗運作結果............................................. 40
4.3 資料探勘結果分析 ............................................................................................... 41
4.3.1 十種預測模型的結果分析........................................................................ 41
4.3.2 預測效能高於十種預測模型結果平均數值的組別探究........................ 43
4.3.3 預測效能低於十種預測模型結果平均數值的組別探究........................ 44
4.4 J48 權重樹狀圖分析............................................................................................. 44
4.4.1 權重排序.................................................................................................... 44
4.4.2 權重樹狀圖探討........................................................................................ 45

第五章 結論與建議........................................................................................................... 47
5.1 研究結論............................................................................................................... 47
5.2 研究限制 ............................................................................................................... 47
5.3 未來研究方向與建議 ........................................................................................... 48
參考文獻..................................................................................................................... 50

VIII

附錄一 IRB 編號: 11106-L03 .......................................................................................... 56
附錄二 不經過 Resample 處理在訓練組的結果 ............................................................. 57
附錄三 不經過 Resample 處理在測試組的結果............................................................. 58
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