臺灣博碩士論文加值系統

隨著科技的進步帶動醫療水準提升，台灣社會人口結構呈現高齡化，高齡人口之眾多死亡原因當中，除慢性病為主要風險因子之一以外;各項疾病中，中風以及慢性腎病這兩項疾病對高齡長者健康影響尤為嚴重; 而中風與慢性腎病的盛行也將對未來台灣醫療體系構成一大隱憂;因此中風與慢性腎病的提前預防與積極治療是目前流行病學研究需審視的一項重大公衛課題。過往雖有研究著墨於中風以及慢性腎病之併發因果關係，卻鮮少有研究運用機器學習方法來建構預測與評估模型。因此，本研究欲嘗試填補這一研究缺口。

以國內某醫療機構資料庫為本研究數據，篩選出罹患中風之病患，以粒子群演算法、基因邏輯斯迴歸演算法、交叉熵演算法、分別計算出各疾病風險因子的權重值，演算法求得之權重值將個別結合倒傳遞類神經 網路與支援向量機建構風險預測模型;個別結合案例式推理技術建構風險評估系統，並設計疾病評估介面，方便使用者進行併發症的風險評估。提出之模型將預測或評估中風病患是否在未來有伴隨慢性腎病的風險。

各預測模型經 K 疊交互驗證結合網格搜索法進行參數調校後，模型效能皆有83%以上的分類準確度，ROC 曲線下面積皆為0.86以上。經傅立曼檢定發現，預測模型之間在分類準確度與ROC曲線下面積，兩效能衡量指標下，皆存在顯著效能差異，因此各模型進一步使用成對樣本T檢定預測模型之個別優劣性。檢定結果發現，在ROC曲線下面積衡量基礎下，以交叉熵結合倒傳遞類神經網路最為優異，ROC曲線下面積達0.9514;在分類準確度衡量基礎下，交叉熵個別結合倒傳遞類神經網路與支援向量機之模型表現同等優異，皆有約92.5%的準確度。評估系統經K疊交互驗證評估其效能，各模型皆有90%以上分類準確度，ROC曲線下面積皆有0.9以上。經傅立曼檢定發現，評估系統在導入上述三類演算法權重值下，在分類準確度與ROC曲線下面積，兩效能衡量指標下，皆不存在顯著效能差異;因此，皆適合作為評估系統之權重運算，本研究可提供相關醫療機構做為預測評估之參考依據。

With the advancement of technology and the improvement of healthcare quality, Taiwan has become an aging society, in which chronic diseases are the leading causes of death. Among chronic diseases, stroke and chronic kidney diseases exert significant adverse impacts not only on individual health but also on the healthcare system of Taiwan. Thus, early intervention and appropriate treatment for such two diseases are of significance in being treated with caution. Although there exists a large and growing body of medical literature establishing the association between stroke and kidney chronic disease, there is a glaring shortfall in medical literature to date: Writings on utilizing existing machine learning techniques to construct prediction/assessment models have remained scant. Addressing such a void is of considerable value.

Enrolling the stroke patients as research subjects, this paper adopts the dataset from the medical institution database. Particle Swarm Optimization (PSO), Genetic Algorithm Logistic Regression (GALR), and Cross-Entropy (CE) algorithm are utilized to compute the weight for the risk factors thus revealing the significance of each risk factor. Three sets of computed weights are individually combined with Back-propagation Neural Network (BPN) and Support Vector Machine (SVM) to construct prediction models, with Case-Based Reasoning (CBR) to construct assessment models (along with a graphical user interface). Proposed models can be adopted to predict/assess whether a certain stroke patient will develop chronic kidney disease in the future.

K-Fold Cross-Validation and Grid Search are combined to conduct hyperparameter tuning for prediction models with all prediction models achieving 83% accuracy and 0.86 AUC. With the Friedman test being conducted, under both two metrics, it is discovered that there exist significant differences among prediction models. Thus, further paired-sample T-test should be conducted to confirm the superiority among models with pairwise comparison. It is concluded that, under AUC metrics, CE combined with BPN achieves the best performance with 0.9154 AUC; under accuracy metrics, CE combined with BPN and with SVM achieve equally strong performance with 92.5% accuracy. K-fold cross-validation is conducted to evaluate the performances of CBR-based assessment models with all models' accuracy being higher than 90% and AURC being higher than 0.9. With the Friedman test being conducted, under both two metrics, it is discovered that there exist no significant differences among assessment models. A further paired-sample T-test is, thus, unnecessary to confirm the superiority among models with pairwise comparison. Therefore, three sets of algorithm weights are all appropriate for being adopted as default weights in assessment models. This paper may serve as a reference for medical professionals and future research by providing robust assessment and prediction models.

摘要 ................................................i
Abstract ..........................................iii
誌謝 .................................................v
目錄 ................................................vi
表目錄 ...............................................x
圖目錄 .............................................xii
第一章.緒論 ............................................1
1.1 研究背景 ..........................................1
1.2 研究動機 ..........................................1
1.3 研究目的 ..........................................2
1.4 研究流程 ..........................................3
第二章.文獻探討 .........................................4
2.1 中風 .............................................4
2.1.1 中風簡介 ........................................4
2.1.2 中風之臨床病症 ...................................5
2.1.3 造成中風的危險因子 ................................6
2.1.4 中風患者之診斷 ...................................7
2.1.5 中風患者之治療 ...................................8
2.1.6 中風的預防 ......................................9
2.2 慢性腎病 .........................................10
2.2.1 慢性腎病簡介 ....................................10
2.2.2 慢性腎病之臨床症狀 ...............................10
2.2.3 造成慢性腎病之風險因子..............................11
2.2.4 慢性腎病之診斷 ..................................13
2.2.5 慢性腎病的病期進展 ...............................14
2.2.6 慢性腎病的治療 ..................................15
2.2.7 慢性腎病的預防 ..................................15
2.3 基因邏輯斯迴歸演算法.................................15
2.3.1 迴歸分析之介紹 ..................................15
2.3.2 邏輯斯迴歸分析之介紹與應用 ........................16
2.3.3 基因演算法之介紹 ................................16
2.3.4 基因演算法演算法流程 ....... .....................17
2.3.5 基因演算法之應用 ................................20
2.3.6 邏輯斯迴歸分析與基因演算法結合 ....................22
2.4 粒子群演算法 .....................................22
2.4.1 粒子群演算法之介紹 ..............................22
2.4.2 粒子群演算法演算法流程 ...........................23
2.4.3 粒子群演算法之應用 ..............................24
2.5 交叉熵演算法 .....................................26
2.5.1 交叉熵演算法之介紹 ..............................26
2.5.2 交叉熵演算法之架構 ..............................27
2.5.3 交叉熵演算法之應用 ..............................28
2.6 支援向量機 .......................................29
2.6.1 支援向量機之介紹 ................................29
2.6.2 支援向量機之架構 ................................29
2.6.3 支援向量機之應用 ................................31
2.7 倒傳遞類神經網路 ..................................32
2.7.1 倒傳遞類神經網路之介紹 ...........................32
2.7.2 倒傳遞類神經網路的架構 ...........................32
2.7.3 類神經網路之應用 ................................34
2.8 案例式推理系統 ....................................35
2.8.1 案例式推理系統之介紹 .............................35
2.8.2 案例式推理系統的架構 .............................35
2.8.3 案例式推理之應用 ................................36
第三章.研究方法 ........................................38
3.1 研究問題 .........................................38
3.1.1 研究對象 ......................................38
3.1.2 研究數據來源 ...................................38
3.2 研究方法 ........................................39
3.2.1 研究架構 ......................................39
3.2.2 研究資料前處理...................................42
3.3 定義研究風險因子變數................................42
3.4 研究風險因子編碼 ..................................43
3.5 數據準備與模型構建 ................................45
3.5.1 研究使用之軟體與數據準備 .........................45
3.5.2 預測模型與評估系統之建構 .........................45
3.6 演算法參數調整 ...................................46
3.6.1 粒子群演算法參數 ...............................46
3.6.2 基因邏輯斯迴歸演算法參數 ........................46
3.6.3 交叉熵演算法參數 .............................47
3.6.4 倒傳遞類神經網路參數 ............................47
3.6.5 支援向量機參數 ...............................49
3.7 預測模型超參數調校與模型效能評估......................50
3.7.1 K疊交互驗證 ..................................50
3.7.2 ROC曲線與ROC曲線下面積 .........................51
3.7.3 混淆矩陣 ...................................52
3.8 模型差異之統計檢定 ...............................54
第四章.研究結果 ......................................56
4.1 資料敘述性統計與視覺化 .............................56
4.2 啟發式演算法參數設定、運算結果與分析....................57
4.2.1 PSO初始參數設定 ................................57
4.2.2 PSO參數調校與求得之風險因子權重值 ...................58
4.2.3 GALR初始參數設定..................................59
4.2.4 GALR參數調校與求得之風險因子權重值 .................59
4.2.5 CE初始參數設定 ................................60
4.2.6 CE參數調校與求得之風險因子權重值.....................60
4.3 預測模型參數設定 ...................................61
4.3.1 SVM初始參數設定 ..................................61
4.3.2 BPN初始參數設定 ..................................62
4.4 預測模型實驗分析 ...................................62
4.4.1 PSO結合SVM之實驗分析 .............................63
4.4.2 GALR結合SVM之實驗分析 ...........................64
4.4.3 CE結合SVM之實驗分析 ...............................65
4.4.4 PSO結合BPN之實驗分析 .............................66
4.4.5 GALR結合BPN之實驗分析 .............................67
4.4.6 CE結合BPN之實驗分析 ...............................68
4.5 各模型效能比較 .....................................70
4.5.1 預測模型實驗結果之比較 ............................70
4.5.2 模型差異性統計檢定 ................................70
4.6 案例式推理系統之建構 ................................71
4.6.1 案例式推理系統運作原理與機制 ........................72
4.6.2 慢性腎病風險評估之圖形使用者介面 .....................72
4.6.3 系統部分操作程序及指令按鈕講解 .......................74
4.6.4 案例式推理評估系統之系統效能驗證 .....................77
第五章.結論 .............................................79

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