運用階層式分群法及加權Apriori探討腦部健檢民眾回診之關聯法則

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研究生：	嗎嘉應 Adinda - Oktavia Kusumanegara
論文名稱：	運用階層式分群法及加權Apriori探討腦部健檢民眾回診之關聯法則 Applying Hierarchical Clustering and Weighted Apriori to Investigate the Examinees' Re-Coming Association Rules
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：	93
中文關鍵詞：	回檢、腦血管健康檢查、華德法分群、權重Apriori
外文關鍵詞：	Examinees’ Re-coming, Cerebrovasular Health Examination, Ward’s Agglomerative, Weighted-Apriori
相關次數：	點閱：325 下載：6
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查詢本校圖書館目錄查詢臺灣博碩士論文知識加值系統勘誤回報

在一些國家，健康檢查是常見的健康照護方法之一。在過去十年，台灣健康照護的服務需求逐漸增加，是由於人口老齡化以及慢性病患樹增加所導致。然而，不可預期的到來，使得醫院難以提供令人滿意的服務。因此，醫院需訂定策略來處理相關問題。應用資料探勘技術在醫療領域可以提供醫院重要資訊。本研究使用群集技術與關聯規則預測腦血管健檢民眾回檢的模式。利用權重Apriori計算各屬性權重值找出規則，可幫助醫院提升服務品質。在執行關聯規則之前，將資料加以分群是必須的過程，因透過分群技術將相似資料聚集在一起後，可在不同群集間找出不同規則。本研究使用華德法階層式分群，此方法容易理解且不需要事先指定分群個數。

General health examinations are common elements of health care in some country. Taiwan demand for healthcare services has increased over the past decade. The increase has been driven by several factors, including an ageing population, and the increasing prevalence of chronic disease. The fluctuation number of examinees with unpredictable coming behavior makes hospital difficult to provide the satisfying service. Hospital needs to make strategic planning such as healthcare management to solve this problem by predicting examinee coming. Data mining applications in healthcare is the realization that data mining can generate information that very useful to all parties involved in the healthcare industry, such as improving the treatment quality of hospitals. This research used clustering and association rule task to know the pattern of cerebrovascular medical examination databases to predict examinees’ re-coming. The Weighted-Apriori algorithm finds out the relationships among item sets using support, confidence, and weight of each feature as the priority rank of the association rule, the characteristic of the rule can be generated, which help the hospital to improve the service quality. The data is performed on partitions that are essentially distinct from each other is the reason why clustering performs before association rule mining is one of essential process. Each cluster would be expected to contain associations without interference or contamination from other sub groupings that have different patterns of relationships. This research used hierarchical clustering method called Ward’s agglomerative which relatively simple to understand, implement, and does not need to specify number of clusters in advance.

Master’s Thesis Recommendation Form ................................................................... ii
Qualification Form by Master’s Degree Examination Committee ....................... iii
摘要............................................................................................................................... iv
ABSTRACT .................................................................................................................. v
ACKNOWLEDGEMENTS ....................................................................................... vi
CONTENTS................................................................................................................ vii
LISTS OF TABLES ..................................................................................................... x
LISTS OF FIGURES .................................................................................................. xi
CHAPTER 1 INTRODUCTION .......................................................................... 1
1 Research Background ..................................................................................... 1
2 Research Objectives ........................................................................................ 3
3 Scope and Constraints ..................................................................................... 3
4 Research Structure .......................................................................................... 3
CHAPTER 2 LITERATURE REVIEW .............................................................. 5
1 Health Examination ........................................................................................ 5
2 Cerebrovascular Disease ................................................................................. 7
3 Risk Factors .................................................................................................... 8
4 State of the Art ................................................................................................ 9
5 Data Mining .................................................................................................. 10
5.1 Ward’s Agglomerative Hierarchical Clustering ............................... 12
5.2 Weighted Apriori .............................................................................. 14
CHAPTER 3 METHODOLOGY ....................................................................... 17
1 Data Collection ............................................................................................. 17
2 Data Preprocessing........................................................................................ 17
2.1 Feature Selection ............................................................................... 19
2.2 Data Cleaning.................................................................................... 19
2.3 Remove Outlier ................................................................................. 19
2.4 Data Normalization ........................................................................... 20
2.5 Data Splitting .................................................................................... 20
2.6 Transforming Data into Categorical Data ......................................... 20
3 Data Mining .................................................................................................. 21
3.1 Ward’s Agglomerative Hierarchical Clustering Method .................. 22
3.2 Apriori ............................................................................................... 23
3.3 Characterization of Frequent Itemsets .............................................. 24
3.4 Weighted-Apriori .............................................................................. 24
4 Data Postprocessing ...................................................................................... 25
CHAPTER 4 RESULTS ANALYSIS AND DISCUSSION .............................. 26
1 Data Collection ............................................................................................. 26
2 Data Preprocessing........................................................................................ 26
2.1 Feature Selection ............................................................................... 27
2.2 Data Cleaning.................................................................................... 28
2.3 Remove Outlier ................................................................................. 28
2.4 Data Splitting .................................................................................... 28
2.5 Data Normalization ........................................................................... 29
2.6 Transform Data into Categorical Data .............................................. 29
3 Data Mining .................................................................................................. 31
3.1 Ward’s Agglomerative Hierarchical Clustering Method .................. 31
3.2 Apriori ............................................................................................... 35
3.3 Weighted-Apriori .............................................................................. 41
4 Data Postprocessing ...................................................................................... 54
4.1 Discussion and Analysis ................................................................... 56
CHAPTER 5 CONCLUSIONS AND FEATURE RESEARCH ...................... 63
5 Conclusions ................................................................................................... 63
2 Contributions................................................................................................. 64
3 Future Research ............................................................................................ 64
REFERENCES ........................................................................................................... 65
APPENDIX A. Apriori Algorithm Results for 2 Comings ..................................... 69
APPENDIX B. Apriori Algorithm Results for 3 Comings ..................................... 71
APPENDIX C. Apriori Algorithm Results for 4 Comings ..................................... 75

                                

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