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研究生： 艾坦琉
Talitha - Octaviani
論文名稱： 運用腦部健診資料探討民眾回診之決策法則
Investigate the Decision Rules of Examinees' Re-coming From a Cerebrovascular Health Checkup Dataset
指導教授： 歐陽超
Chao Ou-Yang
口試委員: 郭人介
Ren-Jieh Kuo
汪漢澄
Han-Cheng Wang
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： 2015
畢業學年度： 103
語文別： 英文
論文頁數： 123
中文關鍵詞： 回檢健檢者 、腦血管健康檢查 、決策樹 、粒子群演算法 、K-means分群法
外文關鍵詞： Examinees’ re-coming, Cerebrovascular Health Examination, Decision Tree, Particle Swarm Optimization, K-means clustering.
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    • 面對多數民眾擔心罹患疾病卻不自知的情況，一般健康檢查應該成為民眾的例行習慣。隨著健康檢查的普遍化，醫院內健檢資料逐漸增加，對此龐大資料，如能正確地應用資料探勘手法進行分析，將可有效的幫助醫院提升醫療品質。在醫療一般健檢上，存在著健檢者是否會回診之情況，本研究應用健檢資料先以粒子群最佳化方法進行特徵擷取，接著以K-means分群法對健檢者進行分，最後決策樹關聯法則將可提供醫院對可能回診之健檢者進行相關之追蹤與行銷。

    The problem faced for most people are being sick without knowing it. Regular health check-ups should be standard procedure in every person’s health routine. Preventive medical check-ups in this context is preventing the cerebrovascular disease, could be understood as one of the condition that enables the re-coming of the examinees besides doing the treatment for the disease. Establishing examinee’s diagnoses often determine the recommendation made to the examinees. The health examination data provides the information needed to make an accurate prediction of the re-coming of the examinee with diagnosis for the examinee’s condition. The condition of the examinee that match the criteria is predicted as do the re-coming and if the rule has the ABD score the examinee indicated as vulnerable to cerebrovascular disease which will have higher chance to come back to the hospital whether to do another checkup for preventing the disease or even doing the treatment for the disease. Knowledge Discovery has the potential to obtain the useful information from the dataset. Decision tree method is one of DM approach that capable to generate decision tree that can be converted to produce comprehensive rules. In generating the rules, the problem occurred is more attribute used more complex the rules would be. In this research, a metaheuristic approach applied in order to maximize the efficiency of the rules generated. The method applied is Particle Swarm Optimization (PSO). This method is applied to do feature selection in order to minimize the error of the tree.

    Master’s Thesis Recommendation Form I Qualification Form by Master’s Degree Examinations Committee II 摘要 III ABSTRACT IV ACKNOWLEDGEMENTS V CONTENTS VI LISTS OF TABLES IX LISTS OF FIGURES X CHAPTER 1 INTRODUCTION 11 1.1 Research Background 11 1.2 Research Objectives 13 1.3 Scope and Constraints 14 1.3.1 Scopes 14 1.3.2 Constraints 14 1.4 Research framework 15 CHAPTER 2 LITERATURE REVIEW 16 2.1 Cerebrovascular Disease 16 2.2 Cerebrovascular Parameter 18 2.3 State of The Art 18 2.4 Data Mining and Knowledge Discovery 21 2.4.1 Knowledge Representation 22 2.4.1.1 Prediction Rules 22 2.4.2 Clustering (K-means Clustering) 22 2.4.2.1 Determining the Number of Cluster (Elbow Method) 24 2.4.3 Data Mining 24 2.4.3.1 Classification 24 2.4.3.2 Classifier Performance Evaluation (k-fold cross-validation) 25 2.4.3.3 Classification Model Validation (Error Rate) 25 2.4.3.4 Decision Tree Induction (CART) 27 2.4.3.5 Rule-Extraction from a Decision Tree 29 2.5 Metaheuristic 30 2.5.1 Particle swarm optimization algorithm (PSO) 30 2.5.2 Binary Particle Swarm Optimization (BPSO) 31 2.5.3 Particle Swarm Optimization for Feature Selection 31 CHAPTER 3 METHODOLOGY 32 3.1 Data collection 34 3.2 Data Pre-processing 34 3.2.1 Feature Reduction 35 3.2.2 Remove the Outliers 35 3.2.3 Data Categorization 35 3.2.4 Clustering Data (K-means clustering) 36 3.3 Data Processing 36 3.3.1 Particle Swarm Optimization 36 3.3.2 Decision Tree Algorithm (CART) 39 CHAPTER 4 IMPLEMENTATION 41 4.1 Data Analysis 41 4.2 Data Preprocessing 44 4.3 Data Processing 47 4.3.1 Discovering the Decision Model for Examinees’ Re-coming 48 4.4 Data Post-processing 52 4.4.1 Discovering the Decision Rules for Examinees’ Re-coming 52 4.4.2 Defining Cluster and Rules Characteristic and ABD Score for each rule 57 4.5 Discussion and Analysis 72 4.5.1 PSODT Analysis 72 4.5.2 Decision Rules Result 72 4.5.3 Predict The Examinee’s Re-coming 96 CHAPTER 5 CONCLUSION AND FUTURE RESEARCH 100 5.1 Conclusion 100 5.2 Contributions 102 5.3 Future Research 102 REFERENCES 104 APPENDIX A. Matlab Source Code 110 APPENDIX B. Best Particles Result 117 APPENDIX C. Lowest Error Decision Tree 121

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