簡易檢索 / 詳目顯示

回結果列表
研究生： 林宛嫻
Wan-Hsien Lin
論文名稱： 整合差分進化K平均演算法與差分進化支援向量迴歸於肝炎病患醫療費用預測之研究
An Integration Of Differential Evolution K-Means Algorithm and Support Vector Regression With Differential Evolution Algorithm For Forecasting Medical Cost Of Patients with Acute Hepatitis
指導教授： 郭人介
Ren-Jieh Kuo
口試委員: 歐陽超
Chao Ou-Yang
蔡介元
Chieh-Yuan Tsai
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： 2014
畢業學年度： 102
語文別： 英文
論文頁數： 54
中文關鍵詞： 肝炎 、預測 、差分進化演算法 、支援向量回歸 、分群
外文關鍵詞： Hepatitis, Forecast, Differential evolution, Support vector regression, Clustering
相關次數： 點閱：337  下載：6 
分享至:
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

    • 台灣是肝炎(Hepatitis)的盛行區域，歷年來，肝細胞癌、肝硬化與慢性肝病都名列國人十大死因之中，因此肝炎疾病不只是國人健康的威脅，也在國家相關醫療費用中佔了很大的負擔。
    有鑑於此，本研究提出一個用於預測之整合差分進化K平均演算與差分進化支援向量回歸的模型，應用於肝炎病患醫療費用預測，先利用主成分分析法(PCA)進行資料的前處理，將資料特徵明顯化，接著再使用差分進化K平均法進行分群處理，各群則會發展出自己的預測模型，減少資料差異性太大或離群值的影響，提高預測的精準度。
    為了驗證本研究所提出之分群架構可有效增加預測的準確性，本研究將比較不同預測演算法在分群與不分群狀況下的準確性。實驗結果證明，結合分群架構的預測模型加強了資料前處理的效果，增加肝炎醫療費用的預測能力。期望透過此研究所提出的分群搭配預測模型能有效幫助使用者(醫生或護理人員)提早作醫院資源的規劃，達到有限資源做最有效率的運用

    Taiwan is an area endemic for chronic hepatitis. The liver cancer is so common that it has ranked the first among cancer mortality since the early 1980’s in Taiwan. Besides, liver cirrhosis and chronic liver diseases are the sixth or seventh in the causes of death. Therefore, due to the active research for hepatitis, it is not only the threat of health, but also a huge medical cost for government.
    In order to develop a predicting system for hospitals to manage the cost of hepatitis patients, this study proposes a two-stage model which integrates differential evolution K-means (DEK-means) algorithm and support vector regression (SVR) with differential evolution. Firstly, principle component analysis (PCA) is applied for data preprocessing in order to extract important features. Then, this study employs DEK-means for clustering and a forecasting model is developed for every cluster individually. With the above process, the proposed model can reduce the impact of outliers and enhance the forecasting accuracy.
    For evaluation, this study compares methods with and without clustering including DE-based SVR, genetic algorithm-based SVR, particle swarm optimization-based SVR, Grid SVR and back-propagation network. Experimental results indicate that the forecasting algorithms with clustering technique have better performance for the data with preprocessing. Thus, this can enhance the ability to forecast medical cost for hepatitis patients. In additional, the computational result shows that this cost forecasting method can really support physicians in planning medical resources and make a good decision to make the most efficient use of limited resources.

    ABSTRACT II ACKNOWLEDGEMENTS III CONTENTS IV LIST OF TABLES VI LIST OF FIGURES VII CHAPTER 1 INTRODUCTION 1 1.1 Research Background 1 1.2 Research Objectives 2 1.3 Research scope and constraints 3 1.4 Research framework 3 CHAPTER 2 LITERATURE REVIRW 5 2.1 Forecasting 5 2.1.1 Forecasting 5 2.1.2 Artificial Neural Network (ANN) 7 2.1.3 Support vectors regression 9 2.1.4 Forecasting accuracy 12 2.2 K-means related methods 12 2.3 Differential evolution 14 2.4 Forecasting combined with clustering method 16 CHAPTER 3 METHODOLOGY 18 3.1 Overview of the proposed method 18 3.2 Data collection 20 3.3 Feature selection 20 3.4 Two-stage forecasting method (DEK-DESVR) 21 3.5 Training and testing the proposed forecasting method 27 CHAPTER 4 Model Evaluation Results and Discussion 29 4.1 Principal component analysis 29 4.2 Clustering stage 30 4.2.1 Cluster number determination 30 4.2.2 Parameter determination for DEK 30 4.3 Forecasting stage 31 4.3.1 DESVR 31 4.3.2 GASVR 34 4.3.3 PSOSVR 35 4.3.4 GridSVR 36 4.3.5 BPN 38 4.4 Summary and Discussion 39 CHAPTER 5 Conclusions and Future research 44 5.1 Conclusions 44 5.2 Contributions 44 5.3 Future Research 45 REFERENCES 46 APPENDIX 51 I. The replication of non-clustering training MSE results 51 II. The replication of non-clustering testing MSE results 52 III. The replication of clustering training MSE results 53 IV. The replication of clustering testing MSE results 54

    Abbass, H. A., Sarker, R., & Newton, C., "PDE: a Pareto-frontier differential evolution approach for multi-objective optimization problems," in Evolutionary Computation, 2001. Proceedings of the 2001 Congress on, 2001, pp. 971-978.
    Abdi, H. & Williams, L. J., "Principal component analysis," Wiley Interdisciplinary Reviews: Computational Statistics, vol. 2, no. 4, pp. 433-459, 2010.
    Armstrong, J., "Research needs in forecasting," International Journal of Forecasting, vol. 4, no. 3, pp. 449-465, 1988.
    Bashir, Z. & El-Hawary, M., "Applying wavelets to short-term load forecasting using PSO-based neural networks," Power Systems, IEEE Transactions on, vol. 24, no. 1, pp. 20-27, 2009.
    Box, G. E. & Pierce, D. A., "Distribution of residual autocorrelations in autoregressive-integrated moving average time series models," Journal of the American Statistical Association, vol. 65, no. 332, pp. 1509-1526, 1970.
    Burke, E. K. & Varley, D., "A genetic algorithms tutorial tool for numerical function optimisation," in ACM SIGCSE Bulletin, 1997, pp. 27-30.
    Chang, P.-C., Liu, C.-H., & Fan, C.-Y., "Data clustering and fuzzy neural network for sales forecasting: A case study in printed circuit board industry," Knowledge-Based Systems, vol. 22, no. 5, pp. 344-355, 2009.
    Chatzis, S. P., "A fuzzy c-means-type algorithm for clustering of data with mixed numeric and categorical attributes employing a probabilistic dissimilarity functional," Expert Systems with Applications, vol. 38, no. 7, pp. 8684-8689, 2011.
    Chen, K.-Y. & Wang, C.-H., "Support vector regression with genetic algorithms in forecasting tourism demand," Tourism Management, vol. 28, no. 1, pp. 215-226, 2007.
    Chen, S.-M., Wang, N.-Y., & Pan, J.-S., "Forecasting enrollments using automatic clustering techniques and fuzzy logical relationships," Expert Systems with Applications, vol. 36, no. 8, pp. 11070-11076, 2009.
    Cui, X., Potok, T. E., & Palathingal, P., "Document clustering using particle swarm optimization," in Swarm Intelligence Symposium, 2005. SIS 2005. Proceedings 2005 IEEE, 2005, pp. 185-191.
    Dalkey, N. & Helmer, O., "An experimental application of the Delphi method to the use of experts," Management science, vol. 9, no. 3, pp. 458-467, 1963.
    Darbellay, G. A. & Slama, M., "Forecasting the short-term demand for electricity: Do neural networks stand a better chance?," International Journal of Forecasting, vol. 16, no. 1, pp. 71-83, 2000.
    Das, S., Abraham, A., Chakraborty, U. K., & Konar, A., "Differential evolution using a neighborhood-based mutation operator," Evolutionary Computation, IEEE Transactions on, vol. 13, no. 3, pp. 526-553, 2009.
    Das, S. & Konar, A., "An improved differential evolution scheme for noisy optimization problems," in Pattern recognition and machine intelligence, ed: Springer, 2005, pp. 417-421.
    Eiben, A. E., Hinterding, R., & Michalewicz, Z., "Parameter control in evolutionary algorithms," Evolutionary Computation, IEEE Transactions on, vol. 3, no. 2, pp. 124-141, 1999.
    Ellis, K., Production Planning and Inventory Control Virginia Tech.: McGraw Hill., 2008.
    Fildes, R., Nikolopoulos, K., Crone, S. F., & Syntetos, A. A., "Forecasting and operational research: A review," Journal of the Operational Research Society, vol. 59, no. 9, pp. 1150-1172, 2008.
    Frey, B. S. & Weck, H., "Estimating the Shadow Economy: A'Naive'Approach," Oxford Economic Papers, vol. 35, no. 1, pp. 23-44, 1983.
    Green, K. C. & Armstrong, J. S., "Structured analogies for forecasting," International Journal of Forecasting, vol. 23, no. 3, pp. 365-376, 2007.
    Guo, H.-x., Zhu, K.-j., Gao, S.-w., & Liu, T., "An improved genetic k-means algorithm for optimal clustering," in Data Mining Workshops, 2006. ICDM Workshops 2006. Sixth IEEE International Conference on, 2006, pp. 793-797.
    He, D., Wang, F., & Mao, Z., "A hybrid genetic algorithm approach based on differential evolution for economic dispatch with valve-point effect," International Journal of Electrical Power & Energy Systems, vol. 30, no. 1, pp. 31-38, 2008.
    Hong, W.-C., "Chaotic particle swarm optimization algorithm in a support vector regression electric load forecasting model," Energy Conversion and Management, vol. 50, no. 1, pp. 105-117, 2009.
    Hong, W.-C., "Electric load forecasting by seasonal recurrent SVR (support vector regression) with chaotic artificial bee colony algorithm," Energy, vol. 36, no. 9, pp. 5568-5578, 2011.
    Hong, W.-C., Dong, Y., Zheng, F., & Wei, S. Y., "Hybrid evolutionary algorithms in a SVR traffic flow forecasting model," Applied Mathematics and Computation, vol. 217, no. 15, pp. 6733-6747, 2011.
    Hoover, J., "How to track forecast accuracy to guide forecast process improvement," Foresight: The International Journal of Applied Forecasting, no. 14, pp. 17-23, 2009.
    Hyndman, R. J. & Koehler, A. B., "Another look at measures of forecast accuracy," International Journal of Forecasting, vol. 22, no. 4, pp. 679-688, 2006.
    Ilonen, J., Kamarainen, J.-K., & Lampinen, J., "Differential evolution training algorithm for feed-forward neural networks," Neural Processing Letters, vol. 17, no. 1, pp. 93-105, 2003.
    Jain, A. K., "Data clustering: 50 years beyond K-means," Pattern Recognition Letters, vol. 31, no. 8, pp. 651-666, 2010.
    Jain, A. K., Mao, J., & Mohiuddin, K. M., "Artificial neural networks: A tutorial," Computer, vol. 29, no. 3, pp. 31-44, 1996.
    Kalyani, S. & Swarup, K. S., "Particle swarm optimization based K-means clustering approach for security assessment in power systems," Expert Systems with Applications, vol. 38, no. 9, pp. 10839-10846, 2011.
    Kedia, V., Thummala, V., & Karlapalem, K., "Time Series Forecasting through Clustering-A Case Study," in COMAD, 2005, pp. 183-191.
    Kennedy, J. & Eberhart, R., "Particle swarm optimization," in Proceedings of IEEE international conference on neural networks, 1995, pp. 1942-1948.
    Kim, K.-j. & Ahn, H., "A recommender system using GA K-means clustering in an online shopping market," Expert systems with applications, vol. 34, no. 2, pp. 1200-1209, 2008.
    Kuo, R. J. & Xue, K. C., "A decision support system for sales forecasting through fuzzy neural networks with asymmetric fuzzy weights," Decision Support Systems, vol. 24, no. 2, pp. 105-126, 1998.
    Kwedlo, W., "A clustering method combining differential evolution with the K-means algorithm," Pattern Recognition Letters, vol. 32, no. 12, pp. 1613-1621, 2011.
    Li, Q., Yuan, G.-Y., Tang, K.-C., Liu, G.-W., Wang, R., & Cao, W.-K., "Prognostic factors for chronic severe hepatitis and construction of a prognostic model," Hepatobiliary Pancreat Dis Int, vol. 7, no. 1, pp. 40-44, 2008.
    Lu, C.-J. & Wang, Y.-W., "Combining independent component analysis and growing hierarchical self-organizing maps with support vector regression in product demand forecasting," International Journal of Production Economics, vol. 128, no. 2, pp. 603-613, 2010.
    Maulik, U. & Bandyopadhyay, S., "Genetic algorithm-based clustering technique," Pattern Recognition, vol. 33, no. 9, pp. 1455-1465, 2000.
    Mellit, A. & Pavan, A. M., "A 24-h forecast of solar irradiance using artificial neural network: Application for performance prediction of a grid-connected PV plant at Trieste, Italy," Solar Energy, vol. 84, no. 5, pp. 807-821, 2010.
    Merwe, D. & Engelbrecht, A. P., "Data clustering using particle swarm optimization," in Evolutionary Computation, 2003. CEC'03. The 2003 Congress on, 2003, pp. 215-220.
    Nasseri, M., Asghari, K., & Abedini, M., "Optimized scenario for rainfall forecasting using genetic algorithm coupled with artificial neural network," Expert Systems with Applications, vol. 35, no. 3, pp. 1415-1421, 2008.
    Onwubolu, G. & Davendra, D., "Scheduling flow shops using differential evolution algorithm," European Journal of Operational Research, vol. 171, no. 2, pp. 674-692, 2006.
    Price, K. V., Storn, R. M., & Lampinen, J. A., "Differential evolution a practical approach to global optimization," 2005.
    Sarin, S., Kumar, A., & Garg, H., "Clinical profile of acute on chronic liver failure (ACLF) and predictors of mortality: a study of 64 patients," Hepatology, vol. 48, p. 450A, 2008.
    Sarin, S. K., Kumar, A., Almeida, J. A., Chawla, Y. K., Fan, S. T., Garg, H., et al., "Acute-on-chronic liver failure: consensus recommendations of the Asian Pacific Association for the study of the liver (APASL)," Hepatology international, vol. 3, no. 1, pp. 269-282, 2009.
    Scholzel, C. & Hense, A., "Probabilistic assessment of regional climate change in Southwest Germany by ensemble dressing," Climate dynamics, vol. 36, no. 9-10, pp. 2003-2014, 2011.
    Simon, H. A., "Theories of decision-making in economics and behavioral science," The American economic review, vol. 49, no. 3, pp. 253-283, 1959.
    Singhal, D. & Swarup, K. S., "Electricity price forecasting using artificial neural networks," International Journal of Electrical Power & Energy Systems, vol. 33, no. 3, pp. 550-555, 2011.
    Srinivasan, D., "Evolving artificial neural networks for short term load forecasting," Neurocomputing, vol. 23, no. 1–3, pp. 265-276, 1998.
    Storn, R., "On the usage of differential evolution for function optimization," in Fuzzy Information Processing Society, 1996. NAFIPS. 1996 Biennial Conference of the North American, 1996, pp. 519-523.
    Storn, R. & Price, K., "Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces," Journal of global optimization, vol. 11, no. 4, pp. 341-359, 1997.
    Vapink, V., The Nature of Statistical Learning Theory. New York: Springer-Verlag, 1995.
    Vapnik, V. N., "An overview of statistical learning theory," Neural Networks, IEEE Transactions on, vol. 10, no. 5, pp. 988-999, 1999.
    Wang, J., Li, L., Niu, D., & Tan, Z., "An annual load forecasting model based on support vector regression with differential evolution algorithm," Applied Energy, vol. 94, no. 0, pp. 65-70, 2012.
    Weigend, A. S. & Rumelhart, D. E., Generalization through minimal networks with application to forecasting: Defense Technical Information Center, 1992.
    Yuhui, S. & Eberhart, R., "A modified particle swarm optimizer," in Evolutionary Computation Proceedings, 1998. IEEE World Congress on Computational Intelligence., The 1998 IEEE International Conference on, 1998, pp. 69-73.
    Zauner, C., Apsner, R., Kranz, A., Kramer, L., Madl, C., Schneider, B., et al., "Outcome prediction for patients with cirrhosis of the liver in a medical ICU: a comparison of the APACHE scores and liver-specific scoringsystems," Intensive care medicine, vol. 22, no. 6, pp. 559-563, 1996.
    Zhang, G. P., Patuwo, B. E., & Hu, M. Y., "A simulation study of artificial neural networks for nonlinear time-series forecasting," Computers & Operations Research, vol. 28, no. 4, pp. 381-396, 2001.
    Zhang, H., Zhu, Y., Zou, W., & Yan, X., "A hybrid multi-objective artificial bee colony algorithm for burdening optimization of copper strip production," Applied Mathematical Modelling, vol. 36, no. 6, pp. 2578-2591, 2012.
    Zhang, L., Xu, X., Zhou, C., Ma, M., & Yu, Z., "An Improved Differential Evolution Algorithm for Optimization Problems," in Advances in Computer Science, Intelligent System and Environment, ed: Springer, 2011, pp. 233-238.
    Zhang, W.-J. & Xie, X.-F., "DEPSO: hybrid particle swarm with differential evolution operator," in Systems, Man and Cybernetics, 2003. IEEE International Conference on, 2003, pp. 3816-3821.
    Zou, H. F., Xia, G. P., Yang, F. T., & Wang, H. Y., "An investigation and comparison of artificial neural network and time series models for Chinese food grain price forecasting," Neurocomputing, vol. 70, no. 16–18, pp. 2913-2923, 2007.

    QR CODE