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研究生： 黃曼欣
Man-Hsin Huang
論文名稱： 應用兩階段模糊神經網路於前列腺癌預後系統之研究
Application of Two-stage Fuzzy Neural Network to Prostate Cancer Prognosis System
指導教授： 郭人介
Ren-Jieh Kuo
口試委員: 王孔政
Kung-Jeng Wang
駱至中
none
學位類別： 碩士
Master
系所名稱： 管理學院 - 工業管理系
Department of Industrial Management
論文出版年： 2012
畢業學年度： 100
語文別： 英文
論文頁數： 129
中文關鍵詞： 預後 、前列腺癌 、最佳化人工免疫網路 、粒子群最佳化演算法 、模糊神經網路
外文關鍵詞： Prognosis, Prostate cancer, Optimization version of artificial immune networ, Particle swarm optimization, Fuzzy neural network
相關次數： 點閱：205  下載：1 
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    • 由於台灣地區男性罹患前列腺癌之死亡人數正在逐年增加，前列腺癌在國內之男性癌症死亡原因及癌症好發率之排名亦呈現年年攀升的現象。然而，目前醫師對於前列腺病人的預後(prognosis)仍是使用五年存活率(five-year survival rate)做為判別的分水嶺，如何利用前列腺癌篩檢中的病理變數去推估較為確切的存活年限成為相當重要的議題。
    有鑑於此，本研究提出一用以預測之兩階段模糊神經網路(two-stage fuzzy neural network, two-stage FNN)。FNN初始之隸屬函數乃是將病理變數分群後計算平均值及變異數所得，然後再結合最佳化人工免疫網路(an optimization version of artificial immune network; Opt-aiNET)與粒子群最佳化演算法(particle swarm optimization; PSO)去學習與訓練FNN之網路架構，藉此找出前列腺病病理變數與存活年限之間的關係。由於結合了人工免疫網路與粒子群最佳化演算法之優點，two-stage FNN具有良好的搜尋能力以避免陷入局部最佳解且收斂速度快。本研究透過三組標竿問題驗證two-stage FNN與其他演算法相較之下確實有較佳之表現。透過模型評估結果亦證明two-stage FNN可更準確預測前列腺癌患者之存活年限。除此之外，本文所提出之方法與人工神經網路不同之處在於，two-stage FNN可藉由模糊中的IF-THEN法則來解釋訓練結果。

    Due to the difficulty of making prognosis for prostate cancer, this study attempts to propose a two-stage fuzzy neural network (FNN) for prediction. The initial membership function parameters of FNN are determined by cluster analysis. Then, an integration of optimization version of artificial immune network (Opt-aiNET) and particle swarm optimization (PSO) is developed to learn the relationship between the prostate cancer clinical features and the prognosis for prostate cancer. The evaluation results for three benchmark data sets first show that the proposed two-stage FNN has better performance than other algorithms. In addition, model evaluation results indicate that the proposed algorithm really can predict the prognosis for prostate cancer more correctly. Besides, unlike artificial neural network, it is much easier to interpret the training results since they are in the form of fuzzy IF-THEN rules.

    ABSTRACT I 摘要 II ACKNOWLEDGEMENTS III CONTENTS IV LIST OF FIGURES VI LIST OF TABLES VIII CHARPTER 1 Introduction 1 1.1 Research Background 1 1.2 Research Objectives 2 1.3 Research Scopes and Constraints 3 1.4 Research Framework 3 CHAPTER 2 Literature Review 5 2.1 Prostate Cancer 5 2.1.1 Cancer Registry Annual Report 5 2.1.2 Process of Diagnosis 5 2.1.3 Critical Pathologic Factors of Prostate Cancer in This Study 6 2.1.4 Classification for Prostate Cancer 9 2.1.5 Predictive Application for Prostate Cancer 10 2.2 Artificial Neural Network 10 2.3 Fuzzy Theory 15 2.3.1 Basic Concept of Fuzzy Sets 15 2.3.2 Fuzzification 18 2.3.3 Defuzzification 18 2.4 Fuzzy Neural Networks 19 2.5 Artificial Immune Network 22 2.6 Particle Swarm Optimization (PSO) 25 2.7 Applications of Soft Computing Techniques to FNN 27 CHAPTER 3 Methodology 30 3.1 Data Collection for Prostate Cancer 31 3.2 Features Selection 32 3.3 Two-Stage Fuzzy Neural Network 32 3.3.1 First Stage- Features Clustering 32 3.3.2 Second Stage- Application of Optimization version of Artificial Immune Network and Particle Swarm Optimization-Based Fuzzy Neural Network 38 CHAPTER 4 Simulation Results 44 4.1 Benchmark Data Set 1- Ackley Function 44 4.1.1 Parameter Determination- Taguchi Method 45 4.1.2 Computational Results 49 4.1.3 Statistical Hypothesis 52 4.2 Benchmark Data Set 2- Hartmann Function 54 4.2.1 Parameter Determination- Taguchi Method 55 4.2.2 Computational Results 58 4.2.3 Statistical Hypothesis 61 4.3 Benchmark Data Set 3- Mackey-Glass Time Series 63 4.3.1 Taguchi Method- Parameter Design 63 4.3.2 Computational Results 66 4.3.3 Statistical Hypothesis 69 CHAPTER 5 Model Evaluation Results and Discussion 72 5.1 Data Collection 72 5.2 Factor Selection- Stepwise Regression 72 5.3 Parameter Determination- Taguchi Method 75 5.4 Prognosis 78 5.4.1 Trial 1 79 5.4.2 Trial 2 84 5.4.3 Trail 3 89 5.4.4 Trail 4 94 5.5 Sensitivity Analysis 98 CHAPTER 6 Conclusion and Future Research 101 6.1 Conclusion 101 6.2 Contributions 101 6.3 Future Research 102 REFERENCES 103 APENDIXES 108 Appendix Ⅰ- The MSE of each algorithm of Ackley function. 108 Appendix Ⅱ- The MSE of each algorithm of Hartmann function. 110 Appendix Ⅲ- The MSE of each algorithm of Mackey-Glass Time Series 112 Appendix Ⅳ- The MSE of each algorithm of Trial 1. 114 Appendix Ⅴ- The MSE of each algorithm of Trial 2. 115 Appendix Ⅵ- The MSE of each algorithm of Trial 3. 116 Appendix Ⅶ- The MSE of each algorithm of Trial 4. 117

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