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Optimized Healthcare Decision Support Method Based on Predictive Mining Techniques

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Optimized Healthcare Decision Support Method Based on Predictive Mining Techniques
論文目錄
 
DEDICATION第1-6頁
ABSTRACT第6-8頁
摘要第8-12頁
ABBREVIATIONS第12-13頁
Table of Contents第13-16頁
Table of Tables第16-17頁
Table of Figures第17-19頁
CHAPTER 1:INTRODUCTION第19-25頁
  1.1 Introduction第19-20頁
  1.2 Motivation第20頁
  1.3 Problem Definition第20-21頁
  1.4 Objectives of the Study第21-22頁
  1.5 Significance of the Study第22-23頁
  1.6 Thesis Outline第23-24頁
  1.7 Permission to Carry out Research第24頁
  1.8 Summary第24-25頁
CHAPTER 2:BACKGROUND AND LITERATURE REVIEW第25-43頁
  2.1 History of BI第26頁
  2.2 The Future of BI第26-29頁
  2.3 Data-Warehouse第29-32頁
    2.3.1 History of Data-Warehouse第31-32頁
  2.4 Decision Support Systems第32-33頁
    2.4.1 Origins of DSS第32-33頁
    2.4.2 DSS Architecture第33頁
  2.5 Data-Mining第33-39頁
    2.5.1 Knowledge Discovery第34頁
    2.5.2 Data Mining Inception第34-36頁
    2.5.3 Modeling Techniques第36頁
    2.5.4 DM Functions第36-38頁
    2.5.5 DM Algorithms第38-39頁
  2.6 Purpose of Literature Review第39頁
  2.7 Literature Review第39-41頁
  2.8 Summary第41-43頁
CHAPTER 3:METHODOLOGY第43-49頁
  3.1 Introduction第43-44頁
  3.2 Strategy for our Predictive Modeling第44-47頁
    3.2.1 The CRISP-DM Model第44-45頁
    3.2.2 Phases and Tasks第45-46頁
    3.2.3 Oracle Data Mining Functionality第46頁
    3.2.4 DM in the Data-Base Kernel advantages observed第46-47頁
    3.2.5 Classification and Prediction Modeling第47頁
  3.3 Summary第47-49頁
CHAPTER 4:DESIGN第49-64頁
  4.1 Introduction第49頁
  4.2 Research Process第49-53頁
    4.2.1 Data Collection第50頁
    4.2.2 Data Pre-Processing and Cleaning第50-51頁
    4.2.3 Data Transformation第51-52頁
    4.2.4 Building a Basic Data-Warehouse第52-53頁
  4.3 DM Design Process第53-57頁
    4.3.1 Software tools Used第54-55頁
    4.3.2 About Oracle & Its Strengths第55-56頁
    4.3.3 Oracle Database 11g Release 2(11.2.0.2)Oracle DM&its Functionality第56-57頁
  4.4 Classification Decision Support Mining Model第57-62頁
  4.5 Summary第62-64頁
CHAPTER 5:IMPLEMENTATION & THE RESULTS第64-87頁
  5.1 Introduction第64頁
  5.2 DSS Architecture第64-69頁
  5.3 Building the Classification and Prediction Model第69-72頁
    5.3.1 Classification and Prediction Model Training第71-72頁
  5.4 Analyzing the Model Results第72-78頁
    5.4.1 Comparing Our OI Classification and Prediction Model第73-74頁
    5.4.2 Overall Performance Analysis for the YES-Class第74頁
    5.4.3 Performance Matrix/Confusion Matrices第74-75頁
    5.4.4 ROC Curve Analysis第75-76頁
    5.4.5 Lift Cumulative Positive & Negative Results Analysis第76-77頁
    5.4.6 Profit Analysis Results第77-78頁
  5.5 Deploying the Support Vector Machine Model第78-86頁
    5.5.1 About the SVM第79-80頁
    5.5.2 How SVM Works第80-82頁
    5.5.3 Results for our OI SVM Prediction Model第82頁
    5.5.4 OI SVM Prediction Model's Performance Matrix第82-85頁
    5.5.5 OI SVM Prediction Model's ROC Analysis Results第85頁
    5.5.6 OI SVM Prediction Model's LIFT Analysis Results第85-86頁
  5.6 Summary第86-87頁
CHAPTER 6:CONCLUSIONS第87-90頁
  6.1 Introduction第87頁
  6.2 Conclusions第87-88頁
  6.3 Limitations of the Study第88頁
  6.4 Recommendations for Future Study第88-90頁
ACKNOWLEDGEMENT第90-91頁
REFERENCES第91-95頁
APPENDICES第95-104頁
APPENDIX A:ABOUT BOTSWANA第95-97頁
APPENDIX B:APPROVAL LETTER FROM THE MINISTRY OF HEALTH第97-98頁
APPENDIX C:DATA MIGRATION SOURCE CODE第98-104頁
APPENDIX D:MODEL BUILDING SOURCE CODE第104頁

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