臺灣博碩士論文加值系統

隨著台灣經濟起飛、國民知識水準及教育水準提高，國人已逐漸重視健康及醫療的問題，民眾的疾病形式也逐漸由傳染病改為癌症及慢性疾病。而根據WHO的資料顯示，腦中風自1990年以來是已開發國家中繼缺血性心臟病、癌症之後的第三大死因。腦中風在台灣一直在十大死因排名中居高不下，國人罹患中風的人數以及因腦中風死亡的人數也逐年不斷增加。腦中風除了是國人成人殘障的第一要因外，更是使用健保資源的前三名疾病，增加社會經濟負擔。而目前診斷腦中風最有效的檢測方式為使用腦部影像檢查或彩色穿顱超音波，然而這些檢查費用所費不貲，民眾如有檢查需求卻無醫師開立診斷證明，只能透過自費進行檢查，使得民眾參與腦部健檢意願降低。
本研究與北部某醫學中心合作，使用此醫學中心所提供的腦部健檢資料，透過資料探勘技術結合超啟發式演算法，包括基因演算法、粒子群演算法及倒傳遞類神經網路，篩選對腦血管病變有重要影響的因子，建立一預測模型，協助醫護人員，對腦血管疾病預測結果，以決定是否應建議健檢民眾進一步進行精密腦部健檢，使罹患腦血管疾病高危險群者，能夠及早發
現病灶，及早治療。

With Taiwan’s economic take-off, Taiwanese people gradually placed importance on the health and medical issues. According to the data reported by WHO, stroke has become a big threat of health in the developed countries since 1999. In Taiwan, stroke is the third of the top ten causes of deaths. Therefore, how to prevent and discover stroke is very important issue now. The best way to examine and diagnose stroke is using the brain image examination and the carotid ultrasound. However, the price of these examinations is excessively higher than others. If people didn’t have any advice from doctor; they have to pay all the expenses for these examinations. It’s the main reason that some people are not willing to do these brain examinations.
Now, we used the brain examination data provided by one hospital which is located in Taipei. We do feature selection and find out which features are important to the cause of stroke by using a hybrid method which is combined with data mining technology and meta-heuristic algorithm (including genetic algorithm, particle swarm optimization and back-propagation network). Finally, we use these features to develop a cerebrovascular disease prediction model. The cerebrovascular disease prediction model can support doctors to give people some advises whether to do the brain examination or not. People can know the state of their brain health, prevent and cure as soon as possible.

摘要 i
Abstract ii
謝誌 iii
目錄 iv
表目錄 vi
圖目錄 vii
第 1 章 緒論 1
1.1 研究背景 1
1.2 研究目的 2
1.3 研究架構 4
1.4研究議題 5
第 2 章 文獻探討 6
2.1 腦血管疾病 6
2.1.1 腦中風 7
2.1.2 影響腦血管疾病之相關因子及疾病 9
2.1.3 腦血管疾病臨床診斷及檢查 10
2.2 資料探勘 12
2.3 類神經網路 13
2.4 基因演算法 13
2.5 粒子群演算法 14
第 3 章 研究方法 16
3.1 研究架構與流程 16
3.2 資料前處理 18
3.3 屬性篩選 19
3.3.1 以基因演算法進行屬性篩選 20
3.3.2 以粒子群演算法進行屬性篩選 24
3.4 倒傳遞類神經網路預測模型 27
第 4 章 研究個案與實驗結果 30
4.1 研究個案與資料前處理 30
4.1.1 研究個案資料介紹 30
4.1.2 資料整理與正規化 31
4.1.3 資料抽樣 35
4.2 預測模型重要屬性篩選 36
4.2.1 基因演算法參數設定 37
4.2.2 粒子群演算法參數設定 39
4.2.3 屬性篩選結果 42
4.2.4 穩健性測試 47
4.3 選出最佳模型 48
第 5 章 結論與建議 50
5.1 結論 50
5.2 研究限制與建議 50
參考文獻 52

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