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題名:資料探勘手術後減重效果分類模式之建構
作者:李宜致 引用關係
作者(外文):Yi-Chih Lee
校院名稱:輔仁大學
系所名稱:商學研究所
指導教授:李天行
李威傑
學位類別:博士
出版日期:2008
主題關鍵詞:資料探勘決策樹類神經網路支援向量機肥胖手術減重Data miningDecision Treeartificial neural networksupport vector machineBariatric surgeryWeight reduction
原始連結:連回原系統網址new window
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肥胖外科手術是治療病態性肥胖患者目前為止唯一持久、有效的方法。透過資料探勘技術來預測減重手術後病患是否能成功減重的文獻是缺乏的,因此本研究利用接受減重手術前病患可以評估的資訊,透過資料探勘的技術預測病患手術後是否能成功的減重。
本研究共收集了251位病患,分別施行了腹腔鏡迷你胃繞道手術或腹腔鏡胃束帶減肥手術。針對同一批病患資料,分別使用了決策樹、類神經網路、支援向量機、羅吉斯迴歸和鑑別分析等建模技術來建置預測減重效果模型,並利用整體分類準確度和整體的誤差成本,比較各類型建模技術的優劣。
在研究病患中,男性有68人,女性183人,持續兩年的減重追蹤後,共有205(81.7%)病患手術後成功減重,只有46位(18.3%)減重失敗。本研究顯示,類神經網路是個較佳的預測技術,整體的預測準確率也優於傳統的統計技術-羅吉斯迴歸與鑑別分析,但若從最小期望誤差成本來挑選工具,決策樹分析技術反而呈現較佳成效。整體而言,資料探勘技術的預測準確率皆高於傳統統計方法。
本研究是極少數利用資料探勘技術去預測肥胖手術後減重效果之研究。透過資料探勘技術除了可以提早預估醫療風險成本外,運用人工智慧預測模型還可以清楚表現出變數之間相對影響性,提供研究者除了模型鑑別準確率外,另外一種輔助判讀的資料,並進一步提供研究的新契機。
Bariatric surgery is the only long-lasting effective treatment to reduce body weight in morbid obesity. Data mining techniques can play a critical role in medical risk cost calculation and decision support because of their effectiveness in multi-factorial analysis and could provide more insights regarding the relative importance among variables. This study used initial evaluations before bariatric surgery and data mining techniques to predict weight outcomes in morbidly obese patients seeking surgical treatment due to the fact that the literature in using data mining techniques to predict weight loss in obese patients who have undergone bariatric surgery is limited.
The objective of this study is to evaluate the appropriateness of using data mining techniques in building weight reduction prediction models using decision tree, artificial neural network, and support vector machine. 251 patients of two different operations including laparoscopic mini-gastric bypass (LMGB) and laparoscopic adjustable gastric banding (LAGB) were evaluated in this study. Analytic results reveal that artificial neural network and decision tree model are better classification models than traditional logistic regression and discriminant analysis in terms of predictive accuracies.
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