臺灣博碩士論文加值系統

全民健保自實施以來，其財務狀況一直令人憂心。健保局也希望透過有效的財務控管來健全其結構。但由於健保資料複雜龐大，傳統的數量方法力有不逮，很難看清楚問題的癥結。本研究利用SVM（Support Vector Machine）及邏輯司迴歸預測分析的方法，嘗試找出健保醫療費用資料中可能的型態。作為一個初步的數量研究，我們選擇了部分中大型醫院加以分類。以醫院醫療費用互相作為training/testing資料集，比較相似/相異 training/testingerrors的想法，將這些醫院區分為四類。同時，我們也評量此分類的適切性及進一步刻畫解釋模式之可行性。

The financial condition of National Health Insurance Program has been deteriorating since its operation。Effective planning and management call for better
understanding about the cost and spending patterns in the
health care data。 However。 utilization of this data set is a challenging statistical task due to its complexity and size。In this study， we use Support Vector Machine (SVM) and logistic regression model to classify the hospitals. For most medium to large hospitals， we compare similarity among them using medical expense information。 Based on
their training and testing errors against each other， we classify these hospitals into four categories。 Discussion on the appropriateness of this categorization and interpretation is also given。

1 簡介
2 背景
2.1 資料.................................................4
2.2 分類工具.............................................5
2.2.1 邏輯迴歸模型(Logistic Regression Model)........5
2.2.2 Support Vector Machine (SVM)...................6
2.2.3 比較...........................................8
3 方法與結果
3.1 研究方法.............................................9
3.2 分類方法............................................20
3.3 實際資料處理........................................23
4 結論與建議
4.1 結論................................................39
4.2 建議................................................40
A 附錄

Agresti, A. (1990).
Categorical Data Analysis. New York: John Wiley

Breiman, L. (1996).
Bagging Predictors.
Machine Learning Journal, Ann. 26, 123-140.

Birnbaum, A. and Maxwell, A. E. (1960).
Classification Procedures Based on Bayes's Formula.
Applied Statistics, 9, 152-169.

Chang, C. C. and Lin, C. J. (2002).
LIBSVM : a Library for Support Vector Machines (Version 2.33).

Chen, R. and Liu, J. S. (1996).
Predictive Updating Methods with Application to Bayesian Classification.
Journal of the Royal Statistical Society B, 58, 397-415.

Cristianini, N. and Shawe-Taylor, J. (2000).
An Introduction to Support Vector Machines.
Cambridge University. Press.

Freund, Y. and Schapire, R. (1996).
Experiments with a New Boosting Algorithm.
In Machine Learning: Proceedings of the Thirteenth International Conference, 148-156.

Friedman, J., Hastie, T. and Tibshirani, R. (2000).
Additive Logistic Regression: A Statistical View of Boosting.
Annals of Statistics, 28, 337-407.

Kokolakis, G. E. (1983).
A New Look at The Problem of Classification with Binary Data.
The Statistician, 32, 144-152.

Krzanowski, W. J. (1975).
Discrimination and Classification Using Both Binary and Continuous Variables.
Journal of the American Statistical Association, 70, 782-790.

Ripley, B. D. (1994).
Neural Networks and Related Methods for Classification.
Journal of the Royal Statistical Society B, 56, 409-456.

Scholkopf, B. and Smola, A. (2001).
Learning With Kernels -- Support Vector Machines, Regularization, Optimization and Beyond.

Su, J. L. and Tseng, Y.L. (2002).
Simulated Comparison on Some Predictors for Binary Sequences :
To Randomize or Not to Randomize ?
Master Thesis, National Dong Hwa University, Hualien, Taiwan.

張鴻仁 (2001).
全民健保 -- 下一步怎麼走.
http://www.nhi.gov.tw

中央健保局 (2001).
全民健保財務問題與解答.
http://www.nhi.gov.tw

趙民德 (2002).
我所知道的一點 Data Mining.
http://cdms.stat.fju.edu.tw