糖尿病已成為二十一世紀重要的公共衛生課題。台灣末期腎臟病盛行率為全世界第一名，且衛生福利部2015年的統計數據指出在2015年的門診重大傷病醫療點數以慢性腎衰竭（尿毒症）為第二高，僅次於癌症；根據中央健康保險局之統計資料，每年有6%健保預算花費在透析治療的費用上，透析治療的患者對於自身家庭財務規劃與全民健保經費之支出有極大的負擔。台灣少有針對糖尿病腎病變相關醫療耗用之探討文章，因此本研究發展糖尿病腎病變患者之醫療資源耗用預測模型，使中央健康保險局以此預測結果作為醫療資源分配的參考依據。
本研究使用五個技術包括多元迴歸、逐步迴歸、多元適應性雲形迴歸(multivariate adaptive regression splines, MARS)、支援向量迴歸(support vector regression, SVR)及兩階段模型(T-SVR)等技術建構預測模型。其中T-SVR模式是先透過逐步迴歸及MARS，篩選出與糖尿病腎病變相關疾病或造成醫療耗用之重要變數，將這些變數聯集後得到SVR模式的預測變數。
實證結果中比較各個模型，得到SVR擁有最佳的績效，其次為T-SVR；且本研究透過逐步迴歸及MARS篩選出的五個重要變數，分別是
高血壓、血脂疾病、心血管疾病、神經病變及排除糖尿病腎病變的腎臟疾病。
本文分析結果除了分析出對於醫療耗用影響甚大之因子以及得到資料探勘技術有較佳的預測績效之外，預測之結果也能使醫療機構管理者進行醫療資源配置與醫療費用控制之依據，使醫療資源的分配可以得到妥善並有效率的運用。

Diabetes has become an important public health issue in the twenty-first century and dialysis treatment has become a large burden on the National Health Insurance (NHI) system of Taiwan, and diabetic nephropathy (DN) is the leading factor that affects whether diabetic patients need dialysis treatment. The rate of end-stage renal disease (ESRD) in Taiwan is the highest in the world. Statistics produced by the Ministry of Health and Welfare in 2015 indicate that chronic kidney failure (uremia) was the second highest cause of visits to primary outpatient clinics in 2015, second only to cancer. According to the National Health Insurance Administration at the Ministry of Health and Welfare, 6% of the health insurance budget is spent on dialysis treatment for ESRD patients. Since there have been few studies on the medical resources consumed by diabetic nephropathy in Taiwan. Therefore, this study proposes a forecasting model for DN patients.
In this study, we used five techniques including multiple regression, stepwise regression, multivariate adaptive regression splines (MARS), support vector
regression (SVR) and a two stage model (T-SVR), to establish a model for predicting the medical resources consumption of diabetic nephropathy patients. To construct the T-SVR model, the input variables we used are the union of variables that are identified as important variables by stepwise regression and MARS for constructing T-SVR model.
The results of comparing these models show that the best performance was odtained usuing SVR, followed by the T-SVR model. In addition, we found five important variables, namely hypertension disease, dyslipidemia disease, cardiovascular disease, cerebrovascular disease and kidney disease excluding DN.
The results in this paper identify the important factors that have a significant impact on medical resources consumption, as well as the model with the best forecasting performance of all the data mining techniques. This study can provide suggestions of medical institutions for allocating medical resources and controlling medical resources consumption, so that the medical resources can be allocated more suitably and effectively.

CHAPTER 1 INTRODUCTION 1
1.1 Research Background 1
1.2 Motivation 3
1.3 Purposes of the study 4
1.4 Study process 5
CHAPTER 2 LITERATURE REVIEW 7
2.1 Medical Resource Consumption 7
2.1.1 Medical Consumption of Diabetic 7
2.1.2 Treatment of DN 8
2.1.3 Medical Consumption of Dialysis 10
2.2 Data Mining 10
2.3 MARS 11
2.4 SVR 13
CHAPTER 3 METHODOLOGY 15
3.1 Conceptual Framework 15
3.2 MARS 23
3.3 SVR 28
3.4.1 Structure of SVR and minimize regularized risk function 30
3.4.2 SVR Parameter Setting 31
CHAPTER 4 EMPIRICAL RESULTS 33
4.1 Descriptive Statistics 33
4.2 Multiple Regression Results 34
4.3 Stepwise Regression Results 35
4.4 MARS Results 37
4.5 SVR Results 38
4.6 T-SVR Results 40
4.7 Models Comparison Results 43
CHAPTER 5 CONCLUSION 44
5.1 Conclusion of the Results 44
5.2 Recommendations for Future Research 45
REFERENCES 47
APPENDIX I 60
APPENDIX II 62
APPENDIX III 72

List of Tables
Page
Table 1-1-1 2006-2016 Dialysis Incident Rate in Taiwan 2
Table 2-1-1 Stages of Diabetic Nephropathy 8
Table 3-1-1 List Of Prediction Variables in Building Prediction Models. 19
Table 3-1-2 Literature of forecasting variables 20
Table 4-1-1 Data describe of objects 33
Table 4-1-2 The Frequency of each Prediction Variables 34
Table 4-2-1 The model result of the Multiple Regression model 35
Table 4-3-1 Result of the Stepwise Regression model(1) 36
Table 4-3-2 Final Result of the Stepwise Regression model 37
Table 4-4-1 Important Prediction Variables of Using MARS 38
Table 4-5-1 Results of SVR Model Parameter Adjustment Combination 39
Table 4-6-1 Results of T-SVR Model Parameter Adjustment Combination 41
Table 4-7-1 Results of Comparing Models 43

List of Figures
Page
Figure 1-4-1 Study Process 6
Figure 3-2-1 Conceptual framework of forecasting model 16
Figure 3-3-1 Piecewise Linear Basis Function 24
Figure 3-3-2 MARS model schematic diagram 26
Figure 3-4-1 Schematic Diagram of SVR 29

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