A Neural Network Approach in Predicting the Blood Glucose Level for Diabetic Patients
Diabetes Mellitus is a chronic metabolic disorder, where the improper management of the blood glucose level in the diabetic patients will lead to the risk of heart attack, kidney disease and renal failure. This paper attempts to enhance the diagnostic accuracy of the advancing blood glucose levels of the diabetic patients, by combining principal component analysis and wavelet neural network. The proposed system makes separate blood glucose prediction in the morning, afternoon, evening and night intervals, using dataset from one patient covering a period of 77 days. Comparisons of the diagnostic accuracy with other neural network models, which use the same dataset are made. The comparison results showed overall improved accuracy, which indicates the effectiveness of this proposed system.
[1] World Health Organization. Available: http://www.who.int
[2] American Diabetes Asscociation. Available: http://www.diabetes.org
[3] Gan, D. editor. Diabetes atlas, 2nd ed. Brussels: International Diabetes
Federation, 2003. Available at
http://www.eatlas.idf.org/webdata/docs/Atlas%202003-Summary.pdf
[4] C.V. Doran, N.H. Hudson,K.T. Moorhead, J.G. Chase, G.M. Shaw, and
C.E. Hann, "Derivative weighted active insulin control modeling and
clinical trials for ICU patients," Medical Engineering and Physics, vol.
26, pp. 855-866, 2004.
[5] P. Dua, F.J. Doyle III, E.N. Pistikopoulos, "Model-based blood glucose
control for type I diabetes via parametric programming," IEEE
Transactions on Biomedical Engineering, vol. 53, pp. 1478-1491,
2006.
[6] C. Owens, H. Zisser, L. Jovanovic, B. Srinivasan, D. Bonvin, and F.J.
Doyle III, "Run-to-run control of blood glucose concentrations for
people with type I diabetes mellitus," IEEE Transactions on
Biomedical Engineering, vol. 53, pp. 996-1005, 2006.
[7] P.G. Fabietti, V. Canonico, M.O. Federici, M. Massimo, and E. Sarti,
"Control oriented model of insulin and glucose dynamics in type I
diabetics," Medical and Biological Engineering and Computing, vol.
44, pp. 69-78, 2006.
[8] M.S. Ibbini, M.A. Masadeh, and M.M.B. Arner, "A semiclosed-loop
optimal control system for blood glucose level in diabetics," Journal of
Medical Engineering and Technology, vol. 28, pp. 189-196, 2004.
[9] B.R. Chang, and H.F. Tsai, "Forecast approach using neural network
adaption to support vector regression grey model and generalized
auto-regressive conditional heteroscedasticity,: Expert Systems with
Application, vol. 34, pp. 925-934, 2008.
[10] M. Engin, S. Demirag, E.Z. Engin, G. Celebi, F. Ersan, E. Asena, and
Colako─ƒlu, "The classification of human tremor signals using artificial
neural network," Expert Systems with Applications, vol. 33, pp.
754-761, 2007.
[11] M. Ture, and I. Kurt, "Comparison of four different time series methods
to forecast hepatitis A virus infection," Expert Systems with
Applications, vol. 31, pp. 41-46, 2006.
[12] A.K. El-Jabali, "Neural network modeling and control of type I
diabetes mellitus," Bioprocess Biosystem Engineering, vol. 27, pp.
75-79, 2005.
[13] T.N. Hung, G. Nejhded, and W.J. Timothy, "Neural-network detection
of Hypoglycemic episodes in children with type I diabetes using
physiological parameters," Proceedings of the 28th IEEE EMBS Annual
International Conference, New York, 2006, pp. 6053-6056.
[14] C. Li, and R. Hu, "PID control based on BP neural network for the
regulation of blood glucose level in diabetes," Proceedings of the 7th
International Conference on Bioinformatics and Bioengineering,
Boston, pp. 1168-1172, 2007.
[15] J.J. Liszka-Hackzell, "Prediction of blood glucose levels in diabetic
patients using a hybrid AI technique," Computers and Biomedical
Research, vol. 32, pp. 132-144, 1999.
[16] S.G. Mougiakakou, A. Prountzou, D. Iliopoulou, K.S. Nikita,
A.Vazeou, and C.S. Bartsocas, "Neural network based glucose-insulin
metabolism models for children with type I diabetes," Proceedings of
the 28th IEEE EMBS Annual International Conference, New York,
2006, pp. 3545-3548.
[17] K. Zarkogianni, S.G. Mougiakakou, A. Prountzou, A. Vazeou, C.S.
Bartsocas, and K.S. Nikita, "An insulin infusion advisory system for
type I diabetes patients based on non-linear model predictive control
methods," Proceedings of the 29th IEEE EMBS Annual International
Conference, Lyon, 2007, pp. 5971-5974.
[18] V. Tresp, T. Briegel, and J. Moody, "Neural-network models for the
blood glucose metabolism for a diabetic," IEEE Transactions on
Neural Networks, vol. 10, pp. 1204-1213, 1999.
[19] D. Dazzi, F. Taddei, A. Gavarini, E. Uggeri, R. Negro, and A.
Pezzarossa, "The control of blood glucose in the critical diabetic
patient: A neuro-fuzzy approach," Journal of Diabetes and Its
Complications, vol. 15, pp. 80-87, 2001.
[20] Phee, H.K., Tung, W.L. & Quek, C. (2007). A personalized approach to
insulin regulation using brain-inspired neural semantic memory in
diabetic glucose control. IEEE Congress on Evolutionary
Computation, Singapore, pp. 2644-2651.
[21] K. Polat, and S. G├╝nes, :An expert system approach based on principal
component analysis and adaptive neuro-fuzzy inference system to
diagnosis of diabetes disease," Digital Signal Processing, vol. 17, pp.
702-710, 2007.
[22] T.N. Hung, G. Nejhded, T.N. Son, and W.J. Timothy, "Detection of
hypoglycemic episodes in children with type I diabetes using an
optimal Bayesian neural network algorithm," Proceedings of the 29th
IEEE EMBS Annual International Conference, Lyon, 2007, pp.
3140-3143.
[23] R. Abu Zitar, "Towards neural network model for insulin/glucose in
diabetics," International Journal of Computing & Information
Sciences, vol. 1, pp. 25-32, 2003.
[24] R. Abu Zitar, and A. Al-Jabali, "Towards neural network model for
insulin/glucose in diabetics-II.," Informatica, vol. 29, pp. 227-232,
2005.
[25] P. Kok, "Prediction blood glucose levels of diabetics using artificial
neural networks," Research Assignment for Master of Science, Delft
University of Technology, 2004.
[26] S.A. Quchani, and E. Tahami, "Comparison of MLP and Elman neural
network for blood glucose level prediction in type I diabetics,"
Proceedings of the 3rd International Federal of Medical and Biological
Engineering, Kuala Lumpur, 2007, pp. 54-58.
[27] G. Baghdadi, and A.M. Nasrabadi, "Controlling blood glucose levels
in diabetics by neural network predictor," Proceedings of the 29th
Annual International Conference of the IEEE EMBS, Lyon, 2007, pp.
3216-3219.
[28] A.J. Richard, and W.W. Dean, Applied multivariate statistical analysis.
New Jersey: Prentice-Hall, 2002, ch 4.
[29] Q. Zhang, "Using wavelet network in nonparametric estimation," IEEE
Transactions on Neural Networks, vol. 8, pp. 227-236, 1997.
[30] Q. Zhang, and A. Beveniste, "Wavelet Networks," IEEE Transactions
on Neural Networks, vol. 3, pp. 889-898, 1992.
[31] B. Biswal, P.K. Dash, B.K. Panigrahi, and J.B.V. Reddy, "Power signal
classification using dynamic wavelet network," Applied Soft
Computing, vol. 9, pp. 118-125, 2009.
[32] S. Srivastava, M. Singh, M. Hanmandlu, and A.N. Jha, "New fuzzy
wavelet neural networks for system identification and control," Applied
Soft Computing, vol. 6, pp. 1-17, 2005.
[33] H. Zhang, B. Zhang, W. Huang, and Q. Tian, "Gabor wavelet
associated memory for face recognition," IEEE Transactions on
Neural Networks, vol. 16, pp. 275-278, 2005.
[34] J.C. Hargreaves, "Timing of ice-age terminations determined by
wavelet methods," Paleoceanography, vol. 18, pp. 1035-1048, 2003.
[1] World Health Organization. Available: http://www.who.int
[2] American Diabetes Asscociation. Available: http://www.diabetes.org
[3] Gan, D. editor. Diabetes atlas, 2nd ed. Brussels: International Diabetes
Federation, 2003. Available at
http://www.eatlas.idf.org/webdata/docs/Atlas%202003-Summary.pdf
[4] C.V. Doran, N.H. Hudson,K.T. Moorhead, J.G. Chase, G.M. Shaw, and
C.E. Hann, "Derivative weighted active insulin control modeling and
clinical trials for ICU patients," Medical Engineering and Physics, vol.
26, pp. 855-866, 2004.
[5] P. Dua, F.J. Doyle III, E.N. Pistikopoulos, "Model-based blood glucose
control for type I diabetes via parametric programming," IEEE
Transactions on Biomedical Engineering, vol. 53, pp. 1478-1491,
2006.
[6] C. Owens, H. Zisser, L. Jovanovic, B. Srinivasan, D. Bonvin, and F.J.
Doyle III, "Run-to-run control of blood glucose concentrations for
people with type I diabetes mellitus," IEEE Transactions on
Biomedical Engineering, vol. 53, pp. 996-1005, 2006.
[7] P.G. Fabietti, V. Canonico, M.O. Federici, M. Massimo, and E. Sarti,
"Control oriented model of insulin and glucose dynamics in type I
diabetics," Medical and Biological Engineering and Computing, vol.
44, pp. 69-78, 2006.
[8] M.S. Ibbini, M.A. Masadeh, and M.M.B. Arner, "A semiclosed-loop
optimal control system for blood glucose level in diabetics," Journal of
Medical Engineering and Technology, vol. 28, pp. 189-196, 2004.
[9] B.R. Chang, and H.F. Tsai, "Forecast approach using neural network
adaption to support vector regression grey model and generalized
auto-regressive conditional heteroscedasticity,: Expert Systems with
Application, vol. 34, pp. 925-934, 2008.
[10] M. Engin, S. Demirag, E.Z. Engin, G. Celebi, F. Ersan, E. Asena, and
Colako─ƒlu, "The classification of human tremor signals using artificial
neural network," Expert Systems with Applications, vol. 33, pp.
754-761, 2007.
[11] M. Ture, and I. Kurt, "Comparison of four different time series methods
to forecast hepatitis A virus infection," Expert Systems with
Applications, vol. 31, pp. 41-46, 2006.
[12] A.K. El-Jabali, "Neural network modeling and control of type I
diabetes mellitus," Bioprocess Biosystem Engineering, vol. 27, pp.
75-79, 2005.
[13] T.N. Hung, G. Nejhded, and W.J. Timothy, "Neural-network detection
of Hypoglycemic episodes in children with type I diabetes using
physiological parameters," Proceedings of the 28th IEEE EMBS Annual
International Conference, New York, 2006, pp. 6053-6056.
[14] C. Li, and R. Hu, "PID control based on BP neural network for the
regulation of blood glucose level in diabetes," Proceedings of the 7th
International Conference on Bioinformatics and Bioengineering,
Boston, pp. 1168-1172, 2007.
[15] J.J. Liszka-Hackzell, "Prediction of blood glucose levels in diabetic
patients using a hybrid AI technique," Computers and Biomedical
Research, vol. 32, pp. 132-144, 1999.
[16] S.G. Mougiakakou, A. Prountzou, D. Iliopoulou, K.S. Nikita,
A.Vazeou, and C.S. Bartsocas, "Neural network based glucose-insulin
metabolism models for children with type I diabetes," Proceedings of
the 28th IEEE EMBS Annual International Conference, New York,
2006, pp. 3545-3548.
[17] K. Zarkogianni, S.G. Mougiakakou, A. Prountzou, A. Vazeou, C.S.
Bartsocas, and K.S. Nikita, "An insulin infusion advisory system for
type I diabetes patients based on non-linear model predictive control
methods," Proceedings of the 29th IEEE EMBS Annual International
Conference, Lyon, 2007, pp. 5971-5974.
[18] V. Tresp, T. Briegel, and J. Moody, "Neural-network models for the
blood glucose metabolism for a diabetic," IEEE Transactions on
Neural Networks, vol. 10, pp. 1204-1213, 1999.
[19] D. Dazzi, F. Taddei, A. Gavarini, E. Uggeri, R. Negro, and A.
Pezzarossa, "The control of blood glucose in the critical diabetic
patient: A neuro-fuzzy approach," Journal of Diabetes and Its
Complications, vol. 15, pp. 80-87, 2001.
[20] Phee, H.K., Tung, W.L. & Quek, C. (2007). A personalized approach to
insulin regulation using brain-inspired neural semantic memory in
diabetic glucose control. IEEE Congress on Evolutionary
Computation, Singapore, pp. 2644-2651.
[21] K. Polat, and S. G├╝nes, :An expert system approach based on principal
component analysis and adaptive neuro-fuzzy inference system to
diagnosis of diabetes disease," Digital Signal Processing, vol. 17, pp.
702-710, 2007.
[22] T.N. Hung, G. Nejhded, T.N. Son, and W.J. Timothy, "Detection of
hypoglycemic episodes in children with type I diabetes using an
optimal Bayesian neural network algorithm," Proceedings of the 29th
IEEE EMBS Annual International Conference, Lyon, 2007, pp.
3140-3143.
[23] R. Abu Zitar, "Towards neural network model for insulin/glucose in
diabetics," International Journal of Computing & Information
Sciences, vol. 1, pp. 25-32, 2003.
[24] R. Abu Zitar, and A. Al-Jabali, "Towards neural network model for
insulin/glucose in diabetics-II.," Informatica, vol. 29, pp. 227-232,
2005.
[25] P. Kok, "Prediction blood glucose levels of diabetics using artificial
neural networks," Research Assignment for Master of Science, Delft
University of Technology, 2004.
[26] S.A. Quchani, and E. Tahami, "Comparison of MLP and Elman neural
network for blood glucose level prediction in type I diabetics,"
Proceedings of the 3rd International Federal of Medical and Biological
Engineering, Kuala Lumpur, 2007, pp. 54-58.
[27] G. Baghdadi, and A.M. Nasrabadi, "Controlling blood glucose levels
in diabetics by neural network predictor," Proceedings of the 29th
Annual International Conference of the IEEE EMBS, Lyon, 2007, pp.
3216-3219.
[28] A.J. Richard, and W.W. Dean, Applied multivariate statistical analysis.
New Jersey: Prentice-Hall, 2002, ch 4.
[29] Q. Zhang, "Using wavelet network in nonparametric estimation," IEEE
Transactions on Neural Networks, vol. 8, pp. 227-236, 1997.
[30] Q. Zhang, and A. Beveniste, "Wavelet Networks," IEEE Transactions
on Neural Networks, vol. 3, pp. 889-898, 1992.
[31] B. Biswal, P.K. Dash, B.K. Panigrahi, and J.B.V. Reddy, "Power signal
classification using dynamic wavelet network," Applied Soft
Computing, vol. 9, pp. 118-125, 2009.
[32] S. Srivastava, M. Singh, M. Hanmandlu, and A.N. Jha, "New fuzzy
wavelet neural networks for system identification and control," Applied
Soft Computing, vol. 6, pp. 1-17, 2005.
[33] H. Zhang, B. Zhang, W. Huang, and Q. Tian, "Gabor wavelet
associated memory for face recognition," IEEE Transactions on
Neural Networks, vol. 16, pp. 275-278, 2005.
[34] J.C. Hargreaves, "Timing of ice-age terminations determined by
wavelet methods," Paleoceanography, vol. 18, pp. 1035-1048, 2003.
@article{"International Journal of Information, Control and Computer Sciences:62998", author = "Zarita Zainuddin and Ong Pauline and C. Ardil", title = "A Neural Network Approach in Predicting the Blood Glucose Level for Diabetic Patients", abstract = "Diabetes Mellitus is a chronic metabolic disorder, where the improper management of the blood glucose level in the diabetic patients will lead to the risk of heart attack, kidney disease and renal failure. This paper attempts to enhance the diagnostic accuracy of the advancing blood glucose levels of the diabetic patients, by combining principal component analysis and wavelet neural network. The proposed system makes separate blood glucose prediction in the morning, afternoon, evening and night intervals, using dataset from one patient covering a period of 77 days. Comparisons of the diagnostic accuracy with other neural network models, which use the same dataset are made. The comparison results showed overall improved accuracy, which indicates the effectiveness of this proposed system.", keywords = "Diabetes Mellitus, principal component analysis,time-series, wavelet neural network.", volume = "3", number = "2", pages = "468-8", }
