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Title: 資料挖掘技術應用於醫療檢驗之研究 以唐氏症之預測為例
Divination of Down''s syndrome using Symbolic Regression
Authors: 駱獻仁
Xian-Ren Luo
Contributors: 吳志宏
Chih-Hung Wu
Keywords: 唐氏症;符號式迴歸;基因演算法;運算函數
Dwon’s Syndrome;Symbolic Regression;Gentic Program
Date: 2004
Issue Date: 2011-05-26 11:09:39 (UTC+8)
Publisher: 高雄市:[樹德科技大學資訊管理研究所]
Abstract: 摘要
唐氏症是一種先天性的遺傳疾病,也是優生保健相當重要的一環。唐氏症可以透過某些生化檢驗指標計算後,而得孕婦懷有唐氏症胎兒的風險機率。以往,類似此種問題大部分皆使用統計技術及比較其他檢驗結果,往往需要大量的成本及人力。本論文透過分析各檢測指標的關係,試圖建立預測唐氏症發生風險機率的模型。使用的技術為Symbolic Regression,是屬於基因演算法的一種,對於非線性的問題可以提供不錯的計算模式。本論文介紹如何將預測唐氏症發生風險機率的問題轉換成Symbolic Regression的形式,並分析相關參數,並以數個實驗驗證此方法。由本研究獲得的預測模型可以獲得82%以上的預測正確性。
Down syndrome is one of the most common genetic birth defects, affecting approximately one in 800 to 1,000 babies. To predict the possibility of such birth defect, tests on blood of pregnant women and comparison on statistical model are performed. This research tries to build models that connect the possibility of Down syndrome with the indicators such as HCG and AFP from experiments. The technique of symbolic regression is employed in this thesis. The model found in by symbolic regression for predicting the possiblility of Down syndrome is of 82% correctness. We present in this these how and why the problem of detecting Down syndrome can be viewed as symbolic regression. The transformation of data and related parameters are discussed. Finally, several experiments are performed and presented.
Appears in Collections:[資訊管理系(所)] 博碩士論文

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