本研究提出一集成式腹主動脈瘤手術後併發症預測模型，本模型以1994年至2008年間進行腹主動脈瘤手術之病患資料進行訓練，本研究結果包括一集成式術後併發症預測模型、術後併發症預測記錄及因果關係決策規則，本模型所計算出之併發症機率與實際發生併發症事實比較，並以接收操作特徵曲線(ROC curve) 進行術後併發症預測模型之準確性評估。經過一系列測試，貝式網路(BN)、類神經網路(NN)及支持向量機(SVM)所集成之模型對於腹主動脈瘤修復術術後併發症預測可提供良好的效能。此外，貝式網路之馬可夫覆蓋提供了以粒子計算所產生的基本決策規則而自然形成之因果關係特徵選取。

This study proposes an ensemble model to predict postoperative morbidity after abdominal aortic surgery. The ensemble model was developed using a training set of consecutive patients who underwent abdominal aortic aneurysm (AAA) repair between 1994 and 2008. The research outcomes consisted of an ensemble model to predict postoperative morbidity, the occurrence of postoperative complications prospectively recorded, and the causal-effect decision rules. The probabilities of complication calculated by the model were compared to the actual occurrence of complications and a receiver operating characteristic (ROC) curve was used to evaluate the accuracy of postoperative morbidity prediction. In this series, the ensemble of BN, NN and SVM models offered satisfactory performance in predicting postoperative morbidity after AAA repair. Moreover, the Markov blankets of BN allow a natural form of causal-effect feature selection, which provides a basis for screening decision rules generated by granular computing.

Table of Contents
Table of Contents	III
List of Figures	IV
List of Tables	V
Chapter 1 Introduction	1
1.1 Research Objectives	4
1.2 Organization of the Dissertation	6
Chapter 2 Review of the Related Work	7
2.1 Data Mining in Medicine	7
2.2 Abdominal Aortic Aneurysms	11
Chapter 3 Methods and Procedures	15
3.1 Materials and Data Preprocessing	16
3.2 Discretization Techniques	17
3.3 Ensemble model	23
3.4 Bayesian network	24
3.5 Rough Set	27
Chapter 4 Results and Discussion	30
4.1 Experiments Results and Analysis	30
4.2 Evaluation the Model	36
Chapter 5 Conclusions and Future Research	39
Reference	41
Appendix A	45

List of Figures
Fig. 1. The proposed architecture	5
Fig. 2. Steps of data mining process	8
Fig. 3. Normal aorta and aortic aneurysms	11
Fig. 4. The trapezoidal fuzzy set of AAA_Size and Creatine	22
Fig. 5. A Markov blanket for EVAR	31
Fig. 6. The partial causality of MB with probability distributions	32
Fig. 7. Fuzzy quantifiers for linguistic summaries	35
Fig. 8. The results of the postoperative morbidity prediction	38

List of Tables
Table 1. Variables used to predict the postoperative morbidity	18

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