REFERENCES
- [1] United States Renal Data System, Annual Data Report: Atlas of Chronic Kidney Disease and End-Stage Renal Disease in the United States, National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD. (2013). http:// www.usrds.org/atlas.aspx. Accessed 26 March 2014.
- [2] Taiwan National Health Insurance Administration, Vital Statistics, (2011). http://www.nhi.gov.tw/webdata/ webdata.aspx?menu=17&menu_id=1023&WD_ID= 1043&webdata_id=805. Accessed 28 June 2012.
- [3] Grangé, S., Hanoy, M., Roy, F. L., Guerrot, D. and Godin, M., “Monitoring of Hemodialysis Quality-ofcare Indicators: Why Is It Important?” BMC Nephrology, Vol. 14, p. 109 (2013). doi: 10.1186/1471-236914-109
- [4] Theofilou, P., “Quality of Life in Patients Undergoing Hemodialysisor Peritoneal DialysisTreatment,”Journal of Clinical Medicine Research, Vol. 3, No. 3, pp. 132138 (2011). doi: 10.4021/jocmr552w
- [5] Kusiak, A., Dixon, B. and Shah, S., “Predicting Survival Time for Kidney Dialysis Patients: a Data Mining Approach,” Computers in Biology and Medicine, Vol. 35, pp. 311327 (2005). doi:10.1016/j.compbiomed. 2004.02.004
- [6] Jen, C. H., Wang, C. C., Jiang, B. C., Chu, Y. H. and Chen, M. S., “Application of Classification Techniques on Development an Early-warning System for Chronic Illnesses,” Expert Systems with Applications, Vol. 39, No. 10, pp. 88528858 (2012). doi: 10.1016/ j.eswa.2012.02.004
- [7] Yeh, J. Y., Wu, T. H. and Tsao, C. W., “Using Data Mining Techniques to Predict Hospitalization of Hemodialysis Patients,” Decision Support Systems, Vol. 50, pp. 439448 (2011). doi: 10.1016/j.dss.2010.11. 001
- [8] Kumar, T. A. A. and Thanamani, A. S., “Techniques to Enhance Productivity in Manufacturing Environments Using Data Mining,” International Journal of Engineering Science Invention, Vol. 2, No. 7, pp. 913 (2013).
- [9] Ting, I. H. and Lin, Y. C., “What Is Missing? Using Data Mining Techniques with Business Cycle Phases for Predicting Company Financial Crises,” Asia Pacific Management Review, Vol. 16, No. 4, pp. 535549 (2011). doi: 10.6126/APMR.2011.16.4.05
- [10] Esfandiari, N., Babavalian, M. R., Moghadam, A. E. and Tabar, V. K., “Knowledge Discovery in Medicine: Current Issue and Future Trend,” Expert Systems with Applications, Vol. 41, No. 9, pp. 44344463 (2014). doi: 10.1016/j.eswa.2014.01.011
- [11] Ganzert,S.and Guttmann,J.,“Analysisof Respiratory Ressure-volume Curves in Intensive Care Medicine Using Inductive Machine Learning,” Artificial Intelligence in Medicine, Vol. 26, No. 12, pp. 6986 (2002). doi: 10.1016/S0933-3657(02)00053-2
- [12] Lin, F. R., Chou, S. P. and Chen, Y., “Mining TimeDependency Patterns in Clinical Pathways,” International Journal of Medical Informatics, Vol. 62, No. 1, pp.1125 (2001). doi: 10.1016/S1386-5056(01)00126-5
- [13] Karabatak, M. and Ince, M. C., “An Expert System for Detection of Breast Cancer Based on Association Rules and Neural Network,” Expert Systems with Applications, Vol. 36, No. 2, pp. 34653469 (2009). doi: 10.1016/j.eswa.2008.02.064
- [14] Kukar, M., Kononenko, I. and Groselj, C., “Analyzing and Improving the Diagnosis of Ischaemic Heart Disease with Machining Learning,” Artificial Intelligence in Medicine, Vol. 16, No. 1, pp. 2550 (1999). doi: 10.1016/S0933-3657(98)00063-3
- [15] Yeh, D. Y., Cheng, C. H. and Chen, Y. W., “A Predictive Model for Cerebrovascular Disease Using Data Mining,” Expert Systems with Applications, Vol. 38, No. 7, pp. 89708977 (2011). doi: 10.1016/j.eswa. 2011.01.114
- [16] National Kidney Foundation, “K/DQOI: Clinical PracticeGuidelinefor Chronic Kidney Disease,”American Journal of Kidney Disease, Vol. 39, Suppl 1, pp. S1 S266 (2002).
- [17] Taiwan Ministry of Health and Welfare, National Health Insurance Medical Quality Indicators Description (2012). http://www.nhi.gov.tw/mqinfo/Content. aspx?List=3&Type=Dialysis. Accessed 2 July 2013.
- [18] Chittaro, L., Combi, C. and Trapasso, G., “Data Mining on Temporal Data: a Visual Approach and Its Clinical Application to Hemodialysis,” Journal of Visual Languages &Computing, Vol. 14, pp. 591620 (2003). doi: 10.1016/j.jvlc.2003.06.003
- [19] Bellazzi, R., Larizza, C., Magni, P. and Bellazzi, R., “Temporal Data Mining for the Quality Assessment of Hemodialysis Services,” Artificial Intelligence in Medicine, Vol.34,pp.2539(2005).doi:10.1016/j.artmed. 2004.07.010
- [20] Bellazzi,R., Sacchi, L., Caffi, E., de Vincenzi, A., Nai, M., Manicone, F., Larizza, C. and Bellazzi,R., “Implementation of an Automated System for Monitoring Adherence to Hemodialysis Treatment: a Report of Seven Years of Experience,” International Journal of Medical Informatics, Vol. 81, No. 5, pp. 320331 (2012). doi: 10.1016/j.ijmedinf.2012.01.007
- [21] Pawlak, Z., “Rough Sets,” International Journal of Computer and Information Sciences, Vol. 11, pp. 341 356 (1982). doi: 10.1007/BF01001956
- [22] Dimitras, A. L., Slowinski, R., Susmaga, R. and Zopounidis, C., “Business Failure Prediction Using Rough Set,” European Journal of Operation Research, Vol. 114, pp. 263280 (1999). doi: 10.1016/S03772217(98)00255-0
- [23] Pawlak, Z., “Rough Set Approach to Knowledge-based Decision Support,” European Journal of Operational Research, Vol. 99, pp. 4857 (1997). doi: 10.1016/ S0377-2217(96)00382-7
- [24] Tsumoto, S., “Automated Extraction of Medical Expert System Rules from Clinical Databases Based on Rough Set Theory,” Information Sciences, Vol. 112, pp. 6784 (1998). doi: 10.1016/S0020-0255(98) 10021-X
- [25] Xie, C. H., Liu, Y. J. and Chang, J. Y., “Medical Image Segmentation Using Rough Set and Local Polynomial Regression,” Multimedia Tools and Applications, Vol. 74, pp. 18851914 (2015). doi: 10.1007/s11042-0131723-2
- [26] Cabena, P., Hadjinian, P., Stadler, R., Verhees, J. and Zanasi,A., Discovering Data Mining: from Concept to Implementation, Prentice Hall, New Jersey (1997).
- [27] Kennedy, L., Lee, Y., Roy, V. B., Reed, C. D. and Lippman, R. P., Solving Data Mining Problems through Pattern Recognition, Prentice Hall, New Jersey (1997).
- [28] Quinlan, J. R., “Induction of Decision Tree,” Machine Learning, Vol. 1, No. 1, pp. 81106 (1986). doi: 10. 1007/BF00116251 [29] Witten, I. H. and Frank, E., Data Mining: Practical Machine Learning Tools with Java Implementations, Morgan Kaufmann, San Francisco, CA. (2000).
- [30] Chang, C. L. and Chen, C. H., “Applying Decision Tree and Neural Network to Increase Quality of Dermatologic Diagnosis,” Expert Systems with Applications, Vol. 36, No. 2, pp. 40354041 (2009). doi: 10. 1016/j.eswa.2008.03.007
- [31] Eom, J. H., Kim, S. C. and Zhang, B. T., “AptaCDSSE: a Classifier Ensemble-based Clinical Decision Support System for Cardiovascular Disease Level Prediction,” Expert Systems with Applications, Vol. 34, No. 4, pp. 24652479 (2008). doi: 10.1016/j.eswa.2007. 04.015
- [32] Breiman, L., Friedman, J., Olshen, R. and Stone, C., Classification and Regression Trees, Wadsworth InternationalGroup, Belmont,CA.(1984). doi:10.2307/2530946 [33] weka.sourceforge.net, Class J48 (2012a). http://weka.sourceforge.net/doc/weka/classifiers/trees/J48.html. Accessed 8 August 2013.
- [34] Cohen, W. W., “Fast Effective Rule Induction,” Proceedings of the 12th International Conference on Machine Learning, Morgan Kaufmann, Lake Tahoe, CA. pp. 115123 (1995).
- [35] Fürnkranz, J. and Widmer, G., “Incremental Reduced Error Pruning,” Proceedings of the 11th International Conference on MachineLearning, Morgan Kaufmann, Lake Tahoe, CA. pp. 7077 (1994).
- [36] weka.sourceforge.net, Class JRip (2012b). http://weka. sourceforge.net/doc.dev/weka/classifiers/rules/JRip. html. Accessed 8 August 2013.
- [37] Frank, E. and Witten,I. H., “Generating Accurate Rule Sets without Global Optimization,”Proceedings of the 15th International Conference on Machine Learning, University of Waikato, Hamilton, New Zealand, pp. 144151 (1998).
- [38] weka.sourceforge.net, Class PART (2012c). http://weka. sourceforge.net/doc.dev/weka/classifiers/rules/ PART.html. Accessed 8 August 2013.
- [39] Brian, R. G. and Compton, P., “Induction of Rippledown Rules Applied to Modeling Large Databases,” Journal of Intelligent Information Systems, Vol. 5, No. 3, pp. 211228 (1995). doi: 10.1007/BF00962234
- [40] weka.sourceforge.net, Class Ridor (2012d). http://weka. sourceforge.net/doc.packages/ridor/weka/classifiers/ rules/Ridor.html. Accessed 8 August 2013.
- [41] Richards, D., “Ripple Down Rules: a Technique for Acquiring Knowledge,”In:ForgionneGA,GuptaJND, Mora M (Eds.) Decision-Making Support Systems: Achievements and Challenges for the New Decade, IRM Press, ch013 (2002). doi: 10.4018/978-1-59140045-5
- [42] Han, J. and Kamber, M., Data Mining: Concepts and Techniques, Morgan Kaufmann, San Francisco, CA. (2006).
- [43] Podgorelec, V., Kokol, P., Stiglic, M. M., Hericko, M. and Rozman, I., “Knowledge Discovery with Classification Rules in a Cardiovascular Dataset,” Computer Methods and Programs in Biomedicine, Vol. 80, pp. S39S49(2005).doi:10.1016/S0169-2607(05)80005-7
- [44] Nemenyi, P. B., Distribution-free Multiple Comparisons, Unpublished doctoral dissertation, Princeton University (1963).
- [45] United States Renal Data System, Annual Data Report: Atlas of End-stage Renal Disease in the United States, National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD. (2002). http://www.usrds.org/atlas.htm. Accessed 18 August 2011.