[1] 林師模、陳苑欽 (2003)。多變量分析,台北市:雙葉。
[2] 陳順宇 (2004)。多變量分析(三版),台北市:華泰。
[3] 曾憲雄、蔡秀滿、蘇東興、曾秋蓉、王慶堯 (2005)。資料探勘 Data mining,台北市:旗標。
[4] 李博智,「資料探勘在慢性病預測模式之建構」,2002,元智大學碩士論文。[5] Cardoso, M., I. H. Themido, et al. (1999). "Evaluating a clustering solution: An application in the tourism market." Intelligent Data Analysis , 3(6): 491-510.
[6] Chae, Y. M., S. H. Ho, et al. (2001). "Data mining approach to policy analysis in a health insurance domain." International journal of medical informatics , 62(2-3): 103-111.
[7] Choi-Kwon, S. and J. S. Kim (1998). "Lifestyle factors and risk of stroke in Seoul, South Korea." Journal of Stroke and Cerebrovascular Diseases, 7(6): 414-420.
[8] Foucan, L., J. Hanley, et al. (2002). "Body mass index (BMI) and waist circumference (WC) as screening tools for cardiovascular risk factors in Guadeloupean women." Journal of clinical epidemiology , 55(10): 990-996.
[9] Groenewald, P. C. N., Mokgatlhe, L. (2005). "Bayesian computation for logistic regression." Computational Statistics and Data Analysis , 48(4): 857-868.
[10] Hunter, A., L. Kennedy, et al. (2000). "Application of neural networks and sensitivity analysis to improved prediction of trauma survival." Computer methods and programs in biomedicine , 62(1): 11-19.
[11] Leoncini, G., G. Sacchi, et al. (2002). "Microalbuminuria identifies overall cardiovascular risk in essential hypertension: an artificial neural network-based approach." Journal of hypertension , 20(7): 1315-1321.
[12] Moshkovich, H. M., A. I. Mechitov, et al. (2002). "Rule induction in data mining: effect of ordinal scales." Expert Systems with Applications, 22(4): 303-311.
[13] Muxfeldt, E. S., K. V. Bloch, et al. (2005). "True resistant hypertension: is it possible to be recognized in the office?" American Journal of Hypertension , 18(12): 1534-1540.
[14] Paternoster, D. M., A. Stella, et al. (1999). "Predictive markers of pre-eclampsia in hypertensive disorders of pregnancy." International Journal of Gynecology and Obstetrics, 66(3): 237-243.
[15] Polak, S. and A. Mendyk (2008). "Artificial neural networks based Internet hypertension prediction tool development and validation." Applied Soft Computing Journal , 8(1): 734-739.
[16] Rakotomamonjy, A., R. Le Riche, et al. (2008). "A comparison of statistical learning approaches for engine torque estimation." Control Engineering Practice, 16(1): 43-55.
[17] Rubin, P. A. (1999). "Adapting the Warmack–Gonzalez algorithm to handle discrete data." European Journal of Operational Research, 113(3): 632-642.
[18] Saito, K. and R. Nakano (2002). "Extracting regression rules from neural networks." Neural Networks , 15(10): 1279-1288.
[19] Su, C. T. and C. H. Yang (2008). "Feature selection for the SVM: An application to hypertension diagnosis." Expert Systems with Applications, 34(1): 754-763.
[20] Ture, M., I. Kurt, et al. (2005). "Comparing classification techniques for predicting essential hypertension." Expert Systems with Applications, 29(3): 583-588.
[21] Zhang, J., A. J. Morris, et al. (1999). "Estimation of impurity and fouling in batch polymerisation reactors through the application of neural networks." Computers and Chemical Engineering, 23(3): 299-312.