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研究生： 慕哈曼
Muhammad - Rieza
論文名稱： 發展一多階數資料探勘方法建立腦中風風險預測模型
Applying Hybrid Data Preprocessing Methods in Stroke Prediction
指導教授： 歐陽超
Chao Ou-Yang
口試委員: 郭人介
Ren-Jieh Kuo
楊朝龍
Chao-Lung Yang
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： 2013
畢業學年度： 101
語文別： 英文
論文頁數： 62
中文關鍵詞： 中風 、不平衡資料 、特徵選擇 、預測方法
外文關鍵詞： Stroke, Imbalance Data, Feature selection, Prediction Method
相關次數： 點閱：201  下載：3 
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    • 中風對於人類健康造成嚴重威脅已成為一世界性問題。腦部影像學檢查以及超音波是目前用以診斷中風的儀器。資料探勘已廣泛用於許多領域，包括醫療工業。資料探勘的使用可幫助醫生預測某些疾患。因此，在這個研究中，多階數資料探勘方法整合了不平衡資料的前期處理、特徵選擇、後傳播網路、支持向量机、決策樹來對於中風進行預測。

    本研究中，腦部影像資料的蒐集時段為2004至2011年。然而，可用資料的不平衡對於預測以及特徵選擇造成了衝擊。這個研究先藉由比較樣本方式對於資料進行“再平衡(rebalance)”處理, Random Under Sampling by Age and RUSboost。除此之外，藉由訊息獲得以及階層式迴歸分析對於平衡資料的重要特色加以篩選。最後，使用後傳播網路、支持向量机 以及決策樹來處理被選擇的特徵以對於中風進行預測。這個多階數資料探勘方法將幫助醫生提供相關訊息給患者。

    Stroke has always been highlighted as a big threat of health in the worldwide. Brain image examination and ultrasound are some alternatives to discover stroke disease. Data mining has been used widely in many areas, including medical industry. The uses of data mining methods allow doctors to make prediction of certain diseases. Therefore, in this research, hybrid model integrating imbalance data preprocessing, feature selection, and back propagation network, support vector machine, decision tree for stroke prediction.
    The dataset used is brain examination data which collected from 2004 to 2011. However, highly imbalance dataset available can impact the performance of prediction as well as feature selected. The study firstly “rebalance” the dataset by comparing sampling methods; Random Under Sampling by Age and RUSboost. In addition, important features of balance training dataset would be selected by information gain and stepwise regression analysis. Towards the end, selected features would be processed using Back Propagation Network, Support Vector Machine and Decision Tree to predict the stroke. These hybrid methods may assist doctor to provide some possibilities information to the patient.

    ABSTRACT III 摘要 IV ACKNOWLEDGMENT V TABLE OF CONTENTS VI LIST OF FIGURES VIII LIST OF TABLES IX CHAPTER I INTRODUCTION 1 1.1 BACKGROUND 1 1.2 PURPOSES 2 1.3 SCOPES AND CONSTRAINTS 3 1.4 RESEARCH FRAMEWORK 3 CHAPTER II LITERATURE REVIEW 5 2.1 CEREBROVASCULAR DISEASE 5 2.1.1 Stroke Risk Factor 6 2.2 DATA MINING TECHNOLOGY 7 2.2.1 Data Preprocessing 9 2.2.2 Predictive Methods 13 CHAPTER III METHODOLOGY 17 3.1 DESIGN PHASE 17 3.2 DATA PREPROCESSING 21 3.2.1 Remove Outlier and Normalization 21 3.2.2 Handling Imbalance Dataset 22 3.2.3 Feature Selection 23 3.3 PREDICTION MODEL 25 3.3.1 Back Propagation Network 25 3.3.2 Support Vector Machine 26 3.3.3 Decision Tree 27 3.4 METHOD EVALUATION 27 CHAPTER IV MODEL IMPLEMENTATION 29 4.1 DATA SETS 29 4.2 DATA PREPROCESSING 31 4.3 HANDLING IMBALANCE DATASETS 32 4.3.1 Random Undersampling by Age Attribute (RUS_Age) 33 4.3.2 Random Undersampling Boost (RUSBoost) 34 4.4 FEATURE SELECTION 35 4.4.1 Information Gain 35 4.4.2 Stepwise Regression Analysis 37 4.5 PREDICTION METHOD 38 4.5.1 Back Propagation Network 38 4.5.2 Support Vector Machine (SVM) 40 4.5.3 Decision Tree 41 4.6 RESULT ANALYSIS AND EVALUATION 41 4.6.1 Feature Selection 41 4.6.2 Prediction Evaluation 44 4.6.3 Statistical Test 45 4.7 DATASET COMPARISON WITH ADDITIONAL FEATURE 47 4.7.1 Data sets 47 4.7.2 Imbalance Dataset and Feature Selection 48 4.7.3 Prediction Method 51 4.7.4 Result Analysis 53 4.8 BEST MODEL ANALYSIS AND DISCUSSION 54 CHAPTER V CONCLUSION AND FUTURE RESEARCH 57 5.1 CONCLUSION 57 5.2 RESEARCH CONTRIBUTION 58 5.3 FUTURE RESEARCH 58 REFERENCES 59

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