隨著科技進步，台灣社會高齡化，現代人的生活壓力越來越大，心血管疾病已不是老年人的專屬疾病，同時， 由於 最近 爆發的嚴重特殊傳染性肺炎，肺炎再度引起了廣泛的關注。近年來，心血管疾病和肺炎的發病率不斷攀升， 兩者疾病皆是全球高死亡率與常見疾病之一，早已 成為公共衛生問題。 醫 療上探討疾病之間的關聯性一直是熱門研究話題 對於預防醫學更具重要參考價值。
本研究以國內某醫療機構資料庫做為研究數據，篩選出罹患心血管疾病之病患，運用基因邏輯斯迴歸演算法、粒子群最佳化演算法與交叉熵演算法 分別計算出各風險因子的權重值並篩選重要因子結合支援向量機、倒傳遞類神經網路與案例式推理技術，建構六組 預測模型與三組評估系統，用以評估後續伴隨肺炎之風險，並設計評估介面，方便醫療人員於臨床上進行併發症的風險評估。
研究結果顯示，預測模型當中以 交叉熵演算法 結合倒傳遞類神經網路 與支援向量機之模型分類準確度表現同等優異，經由K疊交互驗證準確度皆有91.11%以上ROC曲線下面積則以交叉熵演算法結合倒傳遞類神經網路最為優異ROC曲線下面積達0.9537評估系統之三組評估模型經傅立曼檢定發現，在分類準確度與ROC曲線下面積，皆不存在顯著效能差異分類準確度與ROC曲線下面積皆有93%以上與 0.93以上。本研究結果可提供相關醫療機構做為輔助預測評估之參考依據。

With the advancement of technology and the aging of Taiwanese society, coupled with the increasing pressures of modern life, cardiovascular diseases are no longer exclusive to the elderly. Moreover, due to the recent outbreak of the severe acute respiratory syndrome, pneumonia has once again garnered significant attention. In recent years, the incidence rates of both cardiovascular diseases and pneumonia have been steadily rising. These two ailments are among the globally prevalent diseases with high mortality rates, making them longstanding public health concerns. The exploration of interrelationships between diseases has long been a popular topic in medical research and holds significant value as a reference for preventive medicine.

This study utilized a domestic medical institution's database as research data. Patients with cardiovascular diseases were selected and various algorithms, including gene logistic regression, particle swarm optimization, and cross-entropy algorithms, were employed to calculate the weights of different risk factors and identify significant factors. These factors were then combined with support vector machines, backpropagation neural networks, and case-based reasoning techniques to construct six prediction models and three evaluation systems. These models and systems were designed to assess the subsequent risk of pneumonia and facilitate risk assessment of complications in clinical settings.

The research findings revealed that among the prediction models, the combination of the cross-entropy algorithm, backpropagation neural network, and support vector machine demonstrated equally outstanding classification accuracy. Through K-fold cross-validation, the accuracy exceeded 91.11%, and the combination of the cross-entropy algorithm and backpropagation neural network exhibited the most superior area under the ROC curve, reaching 0.9537. The three evaluation models of the assessment system, as determined by the Friedman test, exhibited no significant differences in terms of classification accuracy and area under the ROC curve. Both the classification accuracy and area under the ROC curve exceeded 93% and 0.93, respectively. These research results can serve as reference guidelines for relevant medical institutions in auxiliary predictive assessments.

摘要...i
Abstract...ii
誌謝...iv
目錄...v
表目錄...ix
圖目錄...xi
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 研究範圍...2
1.5 研究流程...3
第二章 文獻探討...4
2.1 心血管疾病...4
2.1.1 心血管疾病簡介...4
2.1.2 心血管疾病之種類...5
2.1.3 心血管疾病臨床症狀與診斷...6
2.1.4 造成心血管疾病的危險因素...8
2.1.5 心血管疾病預防與治療...9
2.2 肺炎...11
2.2.1 肺炎簡介...11
2.2.2 肺炎之診斷與種類...12
2.2.3 造成肺炎的危險因素與預防治療 ...14
2.2.4 肺炎病患治療的現況...16
2.2.5 心血管疾病患者與肺炎相關文獻...17
2.3 粒子群最佳化演算法...17
2.3.1 粒子群最佳化演算法簡介...17
2.3.2 粒子群最佳化演算法流程...18
2.3.3 粒子群最佳化演算法相關文獻...19
2.4 基因邏輯斯迴歸演算法...20
2.4.1 基因演算法簡介...20
2.4.2 基因演算法流程...21
2.4.3 基因演算法相關文獻...26
2.4.4 邏輯斯迴歸分析...27
2.5 基因邏輯斯迴歸演算法...27
2.6 交叉熵演算法...28
2.6.1 交叉熵演算法簡介...28
2.6.2 交叉熵演算法之原理...29
2.6.3 交叉熵演算法相關文獻...29
2.7 支援向量機...30
2.7.1 支援向量機簡介...30
2.7.2 非線性支援向量機...31
2.7.3 支援向量機應用相關文獻...32
2.8 倒傳遞類神經網路...33
2.8.1 倒傳遞類神經網路簡介...33
2.8.2 倒傳遞類神經網路之架構...34
2.8.3 倒傳遞類神經網路相關文獻...35
2.9 案例式推理系統...36
2.9.1 案例式推理系統簡介...36
2.9.2 案例式推理系統架構...36
2.9.3 案例式推理系統相關文獻...38
第三章 研究方法...39
3.1 研究問題...39
3.1.1 研究對象...39
3.1.2 研究資料來源...39
3.2 研究方法...40
3.2.1 研究架構...40
3.2.2 研究資料處理...43
3.3 研究變數定義...43
3.4 變數編碼...44
3.5 模型與系統架構...46
3.6 演算法參數設定...46
3.6.1 粒子群最佳化演算法...46
3.6.2 基因邏輯斯迴歸演算法...47
3.6.3 交叉熵演算法...47
3.6.4 倒傳遞類神經網路...47
3.6.5 支援向量機...48
3.6.6 案例式推理系統...48
3.7 模型驗證與績效評估...49
3.7.1 K疊交互驗證法...49
3.7.2 ROC曲線與ROC曲線下面積...50
3.7.3 混淆矩陣...50
3.8 模型差異之統計檢定...51
第四章 研究結果...53
4.1 案例敘述統計...53
4.2 PSO實驗分析...54
4.2.1 PSO參數設定...54
4.2.2 PSO之權重分析...55
4.3 PSO + BPN實驗分析...56
4.3.1 PSO+BPN參數設定...56
4.3.2 PSO+BPN預測模型分析...57
4.4 PSO + SVM實驗分析...59
4.4.1 PSO+SVM參數設定...59
4.4.2 PSO+SVM之實驗分析...60
4.5 GALR實驗分析...62
4.5.1 GALR參數設定...62
4.5.2 GALR之權重分析...62
4.6 GALR + BPN實驗分析...63
4.6.1 GALR+BPN參數設定...63
4.6.2 GALR+BPN 之實驗分析...64
4.7 GALR + SVM實驗分析...66
4.7.1 GALR+SVM參數設定...66
4.7.2 GALR+SVM之實驗分析...67
4.8 CE實驗分析...69
4.8.1 CE參數設定...69
4.8.2 CE之權重分析...69
4.9 CE + BPN實驗分析...70
4.9.1 CE+BPN參數設定...70
4.9.2 CE+BPN之實驗分析...71
4.10 CE + SVM實驗分析...73
4.10.1 CE+SVM參數設定...73
4.10.2 CE+SVM之實驗分析...74
4.11 研究模型比較...76
4.11.1 研究模型分析結果比較...76
4.11.2 模型差異統計檢定...76
4.12 CBR之建構...78
4.12.1 CBR運作原理與機制...78
4.12.2 肺炎評估系統使用者介面介紹...79
4.12.3 CBR之系統效能驗證...84
第五章 結論...86
參考文獻...88
Extended Abstract...101

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