臺灣博碩士論文加值系統

根據衛生署2000年至2006年統計台灣十大死因資料顯示，癌症位居十大死因之首，其中肝癌更是台灣第一大癌症病因。而慢性肝病及肝硬化為台灣十大死因的第七位。肝病初期症狀不明顯，即使是肝硬化或肝癌等嚴重的肝病，恐怕也要等到病情相當嚴重時才會出現症狀。而過去肝病診斷研究大多以單一肝病類型為主，如慢性活動性肝炎與肝癌，尚無針對全面性肝病類型之診斷模式建立。因此為達到更準確與全面性的肝病醫療診斷及提昇醫療品質與降低診斷成本，本研究之目的為建立智慧型肝病醫療診斷模式 (ILDM)，並收集病患之生化檢驗資料及其生活習慣健康調查資料，運用倒傳遞類神經網路 (BPN) 建構肝病判別模式，來判定是否罹患肝病，再結合層級分析法 (AHP) 及案例式推理 (CBR) 找出指標屬性權重值，來診斷病患罹患肝病類型與其治療方式。研究結果顯示，BPN肝病判別模式其分類平均準確率達98.17% (訓練準確率100%，測試準確率93.94 %)，且CBR的肝病類型診斷準確率達100%。另外，醫師專家以AHP法制定之權重值亦能增加CBR診斷之可信度，並有效提出肝病之藥品採用及治療方法，且醫師可依病患生活習慣改善建議，以輔助肝病病患之治療。CBR除分析罹患肝病類型之可能性外，亦能避免BPN造成誤判病患結果，以達成醫師在肝病診斷推論及治療之實務有效應用。

According to the statistics of Department of Health (DOH) from 2000 to 2006, the cancer is the first disease of the top ten fatal diseases. Among them, the liver cancer is the first cause of disease of the cancer in Taiwan, and the chronic liver diseases and cirrhosis are the seventh of the top ten fatal diseases in Taiwan. The symptoms of liver diseases are not obvious in the initial stage. Even serious liver diseases, such as cirrhosis or the liver cancer, the symptoms will not appear until the condition is quite serious. Most studies of liver diagnosis in the past relied mainly on single type of liver disease, such as the chronic activity hepatitis and liver cancer. There is no liver diagnosis model for all types of liver diseases yet. In order to reach more accurate and complete liver diagnosis, promote medical quality, and reduce the cost of diagnosis, the purpose of this paper is to construct an Intelligent Liver Diagnosis Model (ILDM). Using Back-Propagation Network (BPN), patient’s laboratory data and health survey are collected to construct a liver disease prediction model which is applied to diagnose whether suffer from a liver disease. Then, Analytic Hierarchy Process (AHP) and Case-Based Reasoning (CBR) are combined to find out the weight values of attributes which is used to diagnose the types of liver diseases and to recommend the appropriate prognosis and treatment. The research results indicate that the average accuracy rate of BPN prediction is 98.17% (training set accuracy rate is 100%, testing set accuracy rate is 93.94%) and CBR diagnostic accuracy rate is 100%. Since the professional physicians’ experiences are provided to initialize the weight values of AHP which can improve the credibility of CBR diagnostic, and it recommends the medicines and treatment for the liver diseases more effectively. The physicians can offer suggestions according to patients’ living styles to assist patients’ treatment. Besides of CBR diagnose on probability of liver diseases, it also can reduce diagnostic errors of BPN prediction in order to prove physicians’ diagnose and apply for effective treatment.

中文摘要 i
英文摘要 ii
誌 謝 iv
目 錄 v
表目錄 viii
圖目錄 x
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 2
1.3 研究流程 3
第二章 文獻探討 5
2.1 肝病分類與生化檢驗診斷 5
2.1.1 肝病種類 5
2.1.2 肝病檢驗診斷 5
2.1.3 肝病治療 10
2.1.4 肝病診斷研究應用 11
2.2 生活習慣健康調查 14
2.2.1 生活習慣重要性 14
2.2.2 生活習慣健康調查簡介 15
2.2.3 生活習慣健康調查研究應用 16
2.3 倒傳遞類神經網路 16
2.3.1 倒傳遞類神經網路背景簡介 16
2.3.2 倒傳遞類神經網路優缺點 17
2.3.3 倒傳遞類神經網路於醫學上應用 18
2.4 案例式推理 18
2.4.1 案例式推理基本概念 18
2.4.2 案例式推理優缺點 20
2.4.3 案例式推理於醫學上應用 21
2.5 層級分析法 22
2.5.1 層級分析法簡介 22
2.5.2 層級分析法優缺點 22
2.6 分類決策樹 23
2.6.1 分類決策樹基本概念 23
2.6.2 分類決策樹於醫學上應用 24
2.7 小結 25
第三章 研究設計 26
3.1 研究架構 26
3.2 肝病診斷因子 27
3.3 BPN肝病診斷分析 30
3.3.1 BPN之架構 30
3.3.2 BPN之學習演算法 32
3.3.3 BPN之參數設定 33
3.3.4 BPN之收斂準則 35
3.4 CBR 肝病類型與治療分析 35
第四章 案例分析 43
4.1 BPN肝病診斷判別分析 44
4.1.1 決定網路架構 44
4.1.2 測試參數組合 45
4.1.3 最佳參數組合收斂情況 46
4.2 CBR 肝病類型與治療分析 46
4.2.1 訂定屬性與AHP制定權重 46
4.2.2 建構肝病資料庫與肝病類型案例分析 53
第五章 討論 61
5.1 CBR 肝病類型與治療分析權重制定比較 61
5.2 肝病判別與診斷準確率比較 63
5.3 生化檢驗與全面性肝病診斷因子比較 64
5.4 討論結果分析 65
第六章 結論與建議 66
6.1 結論 66
6.2 管理意涵 66
6.3 限制與建議 67
參考文獻 68
附錄一：國人生活習慣與肝病關聯調查問卷 74
附錄二：肝病診斷因子重要性問卷 78

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