An Efficient Expert System For Diabetes By Naïve Bayesian Classifier

  ijett-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2013 by IJETT Journal
Volume-4 Issue-10
Year of Publication : 2013
Authors : A.Ambica , Satyanarayana Gandi , Amarendra Kothalanka


A.Ambica , Satyanarayana Gandi , Amarendra Kothalanka. "An Efficient Expert System For Diabetes By Naïve Bayesian Classifier". International Journal of Engineering Trends and Technology (IJETT). V4(10):4634-4639 Oct 2013. ISSN:2231-5381. published by seventh sense research group.


In this paper we are proposing an efficient decision support system for Diabetes Disease, apart from the traditional simple support vector machine. We are proposing an efficient two level approach for classifying data. In initial phase we extract optimal feature set from the training data by analyzing the optimality in the dataset, then new dataset is formed as optimal training dataset, now we apply our classification mechanism on the optimal feature set.


[1] D. U. Campos-Delgado, M. Hernandez-Ordonez, R. Femat, and A. Gordillo-Moscoso, .Fuzzy-based controller for glucose regulation in type-1 diabetic patients by subcutaneous route,.IEEE Trans. Biomed. Eng., vol.53, no.11, pp.2201.2210, Nov. 2006.
[2] P. Magni and R. Bellazzi, .A stochastic model to assess the variability of blood glucose time series in diabetic patients self-monitoring,. IEEE Trans. Biomed. Eng., vol. 53, no. 6, pp. 977.985, Jun. 2006.
[3] K. Polat and S. Gunes, .An expert system approach based on principal component analysis and adaptive neuro-fuzzy inference system to diagnosis of diabetes disease,. Dig. Signal Process., vol. 17, no. 4, pp. 702. 710, Jul. 2007.
[4] E. D. Lehmann, “Application of computers in clinical diabetes care,”Diab. Nutr. Metab, vol. 10, pp. 45–59, 1997.
[5] M. G. Kahn, C. A. Abrams, and M. J. Orland, “Intelligent computerbased interpretation and graphical presentation of self-monitored blood glucose and insulin data,” Diab. Nutr. Metab., vol. 4, pp. 99–107, 1991.
[6] S. Andreassen, J. Benn, R. Hovorka, K. G. Olesen, and E. R. Carson, “A probabilistic approach to glucose prediction and insulin dose adjustment: description of metabolic model and pilot evaluation study,” Comput. Meth. Programs Biomed., vol. 41, pp. 153–165, 1994.
[7] R. Bellazzi, P. Magni, and G. De Nicolao, “Bayesian analysis of blood glucose time series from diabetes home monitoring,” IEEE Trans.Biomed. Eng., vol. 47, no. 7, pp. 971–975, Jul. 2000.
[8] S. Montani, P. Magni, R. Bellazzi, C. Larizza, A. V. Roudsari, and E. R. Carson, “Integrating model-based decision support in a multi modal reasoning system for managing type 1 diabetic patients,” Arti. Intell. Med., vol. 29, pp. 131–151, 2003.
[9] J. S. Naylor, A. S. Hodel, A. M. Albisser, J. H. Evers, J. H. Strickland, and D. A. Schumacher, “Comparison of parametrized models for computer-based estimation of diabetic patient glucose response,” Med. Inform., vol. 22, no. 1, pp. 21–34, 1997.
[10] R. Bellazzi, C. Larizza, P. Magni, S. Montani, and M. Stefanelli, “Intelligent analysis of clinical time series: an application in the diabetes mellitus domain,” Artif. Intell. Med., vol. 20, no. 1, pp. 37–57, 2000.
[11] R. Bellazzi, M. Arcelloni, P. Ferrari, P. Decata, M. E. Hernando, A. Garcia, C. Gazzaruso, E. J. Gomez, C. L. C. P. Fratino, and M. Stefanelli, “Management of patients with diabetes through information technology: tools for monitoring and control of the patients’ metabolic behavior,” Diab. Technol. Ther., vol. 6, pp. 567–578, 2004.
[12] C. L. Rohlfing, H. Wiedmeyer, R. R. Little, J. D. England, A. Tennill, and D. E. Goldstein, “Defining the relationship between plasma glucose and HbA1c: analysis of gluose profiles and HbA1c in the diabetes control and complications trial,” Diab. Care, vol. 25, pp. 275–278, 2002.
[14] B. P. Kovatchev, D. J. Cox, A. Kumar, L. Gonder-Frederick, and W. Clarke, “Algorithmic evaluation of metabolic control and risk of severe hypoglycemia in type 1 and type 2 diabetes using self-monitoring blood glucose data,” Diab. Technol. Ther., vol. 5, pp. 817–828, 2003.
[15] M. Muggeo, G. Verlato, E. Bonora, G. Zoppini, M. Corbellini, and E. de Marco, “Long-term instability of fasting plasma glucose, a novel predictor of cardiovascular mortality in elderly patients with noninsulin- dependent Diabetes Mellitus. The Verona diabetes study,” Circulation, vol. 96, pp. 1750–1754, 1997.
[16] M. Muggeo, G. Zoppini, E. Bonora, E. Brun, R. Bonadonna, P. Moghetti, and G. Verlato, “Fasting plasma glucose variability predicts 10-year survival of type 2 diabetic patients: the Verona diabetes study,” Diabetes, vol. 23, pp. 45–50, 2000.
[17] I. B. Hirsch and M. Brownlee, “Should minimal blood glucose variability become the goal standard of glycemic control?,” J. Diab. Its Complications, vol. 19, pp. 178–181, 2005.
[18] F. J. Service, G. D. Molnar, J. W. Rosevear, E. Ackerman, L. C. Gatewood, and W. F. Taylor, “Mean amplitude of glycemic excursions, a measure of diabetic instability,” Diabetes, vol. 19, pp. 644–655, 1970