臺灣博碩士論文加值系統

腦血管疾病是國人健康的大敵，不論是死亡率或致殘率都十分高，除了是前三大死因外，更是青壯年人口失能與殘障的一大主因，因此如何提早預防及盡速發現病徵顯得十分重要。目前在腦血管疾病的檢驗上，仍是以頸部超音波或是腦部影像檢查效果最佳，但是腦部影像檢查通常所費不貲，若是無醫生建議或是明顯病徵出現，民眾進行腦部檢查的意願不大。因此本研究希望以較平價較易取得的一般健康檢查資料為基，運用資料探勘技術，找出一般健診資料與腦中風的關聯性，在一般健診的報告書中，入關於腦部血管的健康情形，提供民眾更多的資訊。

本研究與北部某醫學中心合作，以七年間之腦部健診資料為例，應用複合式資料探勘方法，包括粗糙集、類神經網路等方式，首先在眾多一般健診因子中找出影響腦部血管病變的重要因子，接下來建置腦中風風險輔助預測模型，協助醫生針對民眾一般健診結果，適當給予是否進一步做腦血管檢查的建議，讓健康檢查的效益增到最大；也讓民眾可以察覺自身健康問題，透過預防或盡速治療，降低腦血管疾病所造成的傷害。

Stroke has become a big threat of health for people worldwide, the death rate and
disable rate of stroke are both high. Therefore, how to prevent stroke and discover it is
an important issue now. The best way to examine and discover stroke is the brain
image examination and ultrasound, however, the price of these examinations is
relatively high. People won’t take these examinations if there is no advice from doctor
or no obvious symptom people feel. Consequently, we want to use normal healthy
examination that is cheaper and easy to take to be the basic of our research, using
hybrid data mining techniques to find the association between normal healthy
examination and stroke. And adding some suggestion to the normal healthy
examination report, hope to provide more information to the public.
We use the brain examination data from 2004 to 2011 to develop a
Stroke-Risk-Predicting-Assistance Model by BPN. First, we do the clustering under
sampling, and then find the relative feature by rough set theory, information gain and
gain ratio. Finally, we use Taguchi method to set the best parameter for BPN. The
Stroke-Risk-Predicting-Assistance Model can support doctor to give people some
advise whether to do the brain examination or not, And to maximum the value of
normal healthy examination. People can know their brain health state, and prevent or
cure the stroke as soon as possible.

IV

目錄
摘要................................................................................................................................. I
ABSTRACT ................................................................................................................... II
謝誌............................................................................................................................... III
目錄...............................................................................................................................IV
圖目錄...........................................................................................................................VI
表目錄..........................................................................................................................VII
第一章、緒論................................................................................................................ 1
1.1 研究背景.............................................................................................................. 1
1.2 研究動機.............................................................................................................. 3
1.3 研究目的.............................................................................................................. 5
1.4 論文架構.............................................................................................................. 6
第二章、文獻探討........................................................................................................ 7
2.1 腦血管疾病 ......................................................................................................... 7
2.1.1 腦血管疾病之分類及症狀.......................................................................... 7
2.2 資料探勘............................................................................................................ 12
2.2.1 資料探勘在醫學上的應用......................................................................... 13
2.3 屬性篩選............................................................................................................ 14
2.3.1 粗糙集理論................................................................................................. 15
2.3.2 資訊增益(Information Gain) 與 Gain ratio ............................................. 16
2.4 不平衡資料........................................................................................................ 16
2.5 倒傳遞類神經網路............................................................................................ 17
第三章、研究方法...................................................................................................... 21
3.1 研究架構與流程................................................................................................ 21
3.2 資料收集及前處理............................................................................................ 24
3.2.1 資料前處理................................................................................................ 24
3.3 屬性篩選 ........................................................................................................... 29
3.3.1 粗糙集理論(Rough Set Theory) ................................................................ 30
3.3.3 Gain ratio .................................................................................................... 35
3.4 倒傳遞類神經模型............................................................................................ 37
3.4.1 使用田口方法做BPN 參數設定 ............................................................... 37
3.3.2 建立倒傳遞類神經模型............................................................................ 38
第四章、實做階段...................................................................................................... 39
V

4.1 資料介紹............................................................................................................ 39
4.2 資料前處理........................................................................................................ 42
4.2.1 重複資料與遺漏值處理............................................................................. 42
4.2.2 資料轉換.................................................................................................... 44
4.2.3 處理不平衡資料......................................................................................... 46
4.3 屬性篩選 ........................................................................................................... 51
4.3.1 粗糙集理論................................................................................................ 51
4.3.2 Information Gain 指標 ............................................................................. 54
4.3.3 Information Gain 指標 ............................................................................. 57
4.3.4 各組屬性整理............................................................................................. 60
4.4 建置倒傳遞類神經網路 ................................................................................... 61
4.4.1 田口方法設定參數.................................................................................... 61
4.4.2 個別建立BPN 模型 .................................................................................. 64
4.5 評估 ................................................................................................................... 65
4.5.1 選出最佳模型............................................................................................ 65
4.5.2 驗證模型可行性........................................................................................ 68
4.5.3 分析與討論................................................................................................ 69
第五章、結論與建議.................................................................................................. 75
5.1 結論.................................................................................................................... 75
5.2 未來建議............................................................................................................ 76
參考文獻...................................................................................................................... 77
附錄 A-1、TS_RST 模型之田口實驗結果 .............................................................. 80
附錄 A-2、RD_RST 模型之田口實驗結果 ............................................................. 81
附錄 A-3、 DB_IG 模型之田口實驗結果............................................................... 82
附錄 A-4、 TS_IG 模型之田口實驗結果 ............................................................... 83
附錄 A-5、 RD_IG 模型之田口實驗結果............................................................... 84
附錄 A-6、 DB_GR 模型之田口實驗結果 ............................................................. 85
附錄 A-7、TS_IG 模型之田口實驗結果 ................................................................. 86
附錄 A-8、 RD_GR 模型之田口實驗結果 ............................................................. 87

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