Comparison of Different Neural Network Approaches for the Prediction of Kidney Dysfunction
This paper presents the prediction of kidney
dysfunction using different neural network (NN) approaches. Self
organization Maps (SOM), Probabilistic Neural Network (PNN) and
Multi Layer Perceptron Neural Network (MLPNN) trained with Back
Propagation Algorithm (BPA) are used in this study. Six hundred and
sixty three sets of analytical laboratory tests have been collected from
one of the private clinical laboratories in Baghdad. For each subject,
Serum urea and Serum creatinin levels have been analyzed and tested
by using clinical laboratory measurements. The collected urea and
cretinine levels are then used as inputs to the three NN models in
which the training process is done by different neural approaches.
SOM which is a class of unsupervised network whereas PNN and
BPNN are considered as class of supervised networks. These
networks are used as a classifier to predict whether kidney is normal
or it will have a dysfunction. The accuracy of prediction, sensitivity
and specificity were found for each type of the proposed networks
.We conclude that PNN gives faster and more accurate prediction of
kidney dysfunction and it works as promising tool for predicting of
routine kidney dysfunction from the clinical laboratory data.
[1] S. Klahr, S Miller and S. B. Miller. "Acute oliguria". The New England
Journal of Medicine, Volume 338, no. 10 pp.671-675, 1998.
[2] T. W. Meyer and T. H. Hostetter, "Uremia," The New England Journal
of Medicine, Volume 357, no. 13 pp.1316, 2007. .
[3] A. H. A. Al-Timemy, F. M. Al-Naima and S. Mahdi, "Data acquisition
system for myocardial infarction classification based on wavelets and
neural networks," in Proc. of the Fifth International Multi-Conference
on Systems, Signals and Devices (IEEE SSD'08), Amman, Jordan,
2008.
[4] M. Chester, Neural networks: a tutorial, Englewood Cliffs, NJ: Prentice
and Hall, 1993, ch.2.
[5] K. Hornik, M. Stinchcombe and H. White," Multilayer feed forward
networks are universal approximators Neural Networks," Journal of
Neural Networks, Vol. 2, No. 5., pp. 359-366, 1989.
[6] J. A. Scott and E. L. Palmer, "Neural network analysis of ventilationperfusion
lung scans," Journal of Radiology; Volume 186, pp. 661-664,
1993.
[7] W. G. Baxt, " Use of an artificial neural network for the diagnosis of
myocardial infarction", Ann Intern Med, volume 115, no. 11, pp. 843-
848, Dec. 1991.
[8] J. V. Tu and M. R. J. Guerriere, "Use of a neural network as a predictive
instrument for length of stay in the intensive care unit following cardiac
surgery", presented at the 16th symposium on computer applications in
medical care (SCAMC), Computers and Biomedical Research, volume
26, issue 3, pp. 220-229, 1993.
[9] M. Green, J. Bjork, J. Forberg, U. Ekelund, L. Edenbrandt and M.
Ohlsson, "Comparison between neural networks and multiple logistic
regression to predict acute coronary syndrome in the emergency room,"
Artificial Intelligence in Medicine, Volume 38, pp. 305-318, 2006.
[10] A. Peled, "Plasticity imbalance in mental disorders the neuroscience of
psychiatry: Implications for diagnosis and research," Medical
Hypotheses, Volume 65, pp.947-952, 2005.
[11] E. Politi, C. Balduzzi, R. Bussi and L. Bellodi, " Artificial neural
networks: A study in clinical psychopharmacology," Psychiatry
Research, Volume 87, pp. 203-215, 1999.
[12] K. Suzuki, J. Shiraishi, H. Abe, H. MacMahon and K. Doi, "Falsepositive
reduction in computer-aided diagnostic scheme for detecting
nodules in chest radiographs by means of massive training artificial
neural network," Academic Radiology, Volume 12, pp. 191-201,2005.
[13] J. T. Batuello, E. J. Gamito, E. D. Crawford, M. and Han, A. W. Partin
D. G. McLeod,"Artificial neural network model for the assessment of
lymph node spread in patients with clinically localized prostate cancer,"
Urology, Volume 57, pp. 481-485, 2005.
[14] T. Kohonen. "Self-Organization and Associative Memory". Third
Edition, Springer-Verlag, Berlin Heidelberg, 1989, pp. 18-67.
[15] T. Kohonen. " Self-Organization Maps". Springer-Verlag, Berlin
Heidelberg, 1995, pp. 23-82.
[16] T. Yanaglda, T. Miura and I. Shioya, "Classifying news corpus by selforganizing
maps", presented at IEEE Pacific Rim Conference on
Communications, Computers and signal Processing, PACRIM. 2003.
[17] D. F. Specht, "Enhancements to Probabilistic Neural Networks", in
Proc. of International Joint Conference on Neural Networks, (1), 1992,
pp. 525- 532.
[18] P. Burrascano, "Learning Vector Quantization For The Probabilistic
Neural Network", IEEE Transactions on Neural Networks, 2(4): 458-
461, 1991.
[19] S. Chen, , C.F.N. Cowan, P.M. Grant, "Orthogonal least squares
learning algorithm for radial basis function networks", IEEE
Transactions on Neural Networks, 2(2): 302-309, 1991.
[20] N. I. A. Wahab, A. Mohamed and A. Hussain, "An Improved Method in
Transient Stability Assessment of a Power System Using Probabilistic
Neural Network", Journal of Applied Sciences Research, 3(11), pp.
1267-1274, 2007.
[21] N. Barycy et. al, "Classification of Mitral Insufficiency and Stenosis
Using MLP Neural Network and Neuro-Fuzzy System", Journal of
Medical Systems, Vol. 28, No. 5, October 2004 .
[22] G. K. Prasad and J. S. Sahamb " Classification of ECG Arrhythmias
using Multi-Resolution Analysis and Neural Networks ",IEEE
Transaction on Biomedical Engineering, 2003.
[23] M. I. Owis, A. H. Abu-Zied, A. B. M. Youssef, and Y. M. Kadah,
''Study of Features Based on Nonlinear Dynamical Modeling in ECG
Arrhythmia Detection and Classification '' ,IEEE Transactions on
Biomedical Engineering ,Vol. 49,No. 7, July 2002.
[24] R. Acharya, A. Kumar , P. S. Bhat, C. M. Lim S. ,S. Iyengar, N.
Kannathal,and S. M. Krishnan ," Classification of Cardiac
Abnormalities using Heart Rate Signals", Medical & Biological
Engineering & Computing, Vol. 42, 2004.
[25] A. H. A. Al-Timemy, "Self-Organization Maps for Prediction of
Kidney Dysfunction", In Proc. 16th Telecommunications Forum
TELFOR, Belgrade, Serbia, 2008.
[26] N. Belgacem, M. A. Chikh, A. Chikh, F. B. Reguig , Applications of
neural nets to detect atrial premature beat," Laboratory Report of
Biomedical Engineering, Department of Electronics, Faculty of Science
and Engineering, University Abou Bekr Belkaïd, Tlemcen, Algeria,
2002.
[1] S. Klahr, S Miller and S. B. Miller. "Acute oliguria". The New England
Journal of Medicine, Volume 338, no. 10 pp.671-675, 1998.
[2] T. W. Meyer and T. H. Hostetter, "Uremia," The New England Journal
of Medicine, Volume 357, no. 13 pp.1316, 2007. .
[3] A. H. A. Al-Timemy, F. M. Al-Naima and S. Mahdi, "Data acquisition
system for myocardial infarction classification based on wavelets and
neural networks," in Proc. of the Fifth International Multi-Conference
on Systems, Signals and Devices (IEEE SSD'08), Amman, Jordan,
2008.
[4] M. Chester, Neural networks: a tutorial, Englewood Cliffs, NJ: Prentice
and Hall, 1993, ch.2.
[5] K. Hornik, M. Stinchcombe and H. White," Multilayer feed forward
networks are universal approximators Neural Networks," Journal of
Neural Networks, Vol. 2, No. 5., pp. 359-366, 1989.
[6] J. A. Scott and E. L. Palmer, "Neural network analysis of ventilationperfusion
lung scans," Journal of Radiology; Volume 186, pp. 661-664,
1993.
[7] W. G. Baxt, " Use of an artificial neural network for the diagnosis of
myocardial infarction", Ann Intern Med, volume 115, no. 11, pp. 843-
848, Dec. 1991.
[8] J. V. Tu and M. R. J. Guerriere, "Use of a neural network as a predictive
instrument for length of stay in the intensive care unit following cardiac
surgery", presented at the 16th symposium on computer applications in
medical care (SCAMC), Computers and Biomedical Research, volume
26, issue 3, pp. 220-229, 1993.
[9] M. Green, J. Bjork, J. Forberg, U. Ekelund, L. Edenbrandt and M.
Ohlsson, "Comparison between neural networks and multiple logistic
regression to predict acute coronary syndrome in the emergency room,"
Artificial Intelligence in Medicine, Volume 38, pp. 305-318, 2006.
[10] A. Peled, "Plasticity imbalance in mental disorders the neuroscience of
psychiatry: Implications for diagnosis and research," Medical
Hypotheses, Volume 65, pp.947-952, 2005.
[11] E. Politi, C. Balduzzi, R. Bussi and L. Bellodi, " Artificial neural
networks: A study in clinical psychopharmacology," Psychiatry
Research, Volume 87, pp. 203-215, 1999.
[12] K. Suzuki, J. Shiraishi, H. Abe, H. MacMahon and K. Doi, "Falsepositive
reduction in computer-aided diagnostic scheme for detecting
nodules in chest radiographs by means of massive training artificial
neural network," Academic Radiology, Volume 12, pp. 191-201,2005.
[13] J. T. Batuello, E. J. Gamito, E. D. Crawford, M. and Han, A. W. Partin
D. G. McLeod,"Artificial neural network model for the assessment of
lymph node spread in patients with clinically localized prostate cancer,"
Urology, Volume 57, pp. 481-485, 2005.
[14] T. Kohonen. "Self-Organization and Associative Memory". Third
Edition, Springer-Verlag, Berlin Heidelberg, 1989, pp. 18-67.
[15] T. Kohonen. " Self-Organization Maps". Springer-Verlag, Berlin
Heidelberg, 1995, pp. 23-82.
[16] T. Yanaglda, T. Miura and I. Shioya, "Classifying news corpus by selforganizing
maps", presented at IEEE Pacific Rim Conference on
Communications, Computers and signal Processing, PACRIM. 2003.
[17] D. F. Specht, "Enhancements to Probabilistic Neural Networks", in
Proc. of International Joint Conference on Neural Networks, (1), 1992,
pp. 525- 532.
[18] P. Burrascano, "Learning Vector Quantization For The Probabilistic
Neural Network", IEEE Transactions on Neural Networks, 2(4): 458-
461, 1991.
[19] S. Chen, , C.F.N. Cowan, P.M. Grant, "Orthogonal least squares
learning algorithm for radial basis function networks", IEEE
Transactions on Neural Networks, 2(2): 302-309, 1991.
[20] N. I. A. Wahab, A. Mohamed and A. Hussain, "An Improved Method in
Transient Stability Assessment of a Power System Using Probabilistic
Neural Network", Journal of Applied Sciences Research, 3(11), pp.
1267-1274, 2007.
[21] N. Barycy et. al, "Classification of Mitral Insufficiency and Stenosis
Using MLP Neural Network and Neuro-Fuzzy System", Journal of
Medical Systems, Vol. 28, No. 5, October 2004 .
[22] G. K. Prasad and J. S. Sahamb " Classification of ECG Arrhythmias
using Multi-Resolution Analysis and Neural Networks ",IEEE
Transaction on Biomedical Engineering, 2003.
[23] M. I. Owis, A. H. Abu-Zied, A. B. M. Youssef, and Y. M. Kadah,
''Study of Features Based on Nonlinear Dynamical Modeling in ECG
Arrhythmia Detection and Classification '' ,IEEE Transactions on
Biomedical Engineering ,Vol. 49,No. 7, July 2002.
[24] R. Acharya, A. Kumar , P. S. Bhat, C. M. Lim S. ,S. Iyengar, N.
Kannathal,and S. M. Krishnan ," Classification of Cardiac
Abnormalities using Heart Rate Signals", Medical & Biological
Engineering & Computing, Vol. 42, 2004.
[25] A. H. A. Al-Timemy, "Self-Organization Maps for Prediction of
Kidney Dysfunction", In Proc. 16th Telecommunications Forum
TELFOR, Belgrade, Serbia, 2008.
[26] N. Belgacem, M. A. Chikh, A. Chikh, F. B. Reguig , Applications of
neural nets to detect atrial premature beat," Laboratory Report of
Biomedical Engineering, Department of Electronics, Faculty of Science
and Engineering, University Abou Bekr Belkaïd, Tlemcen, Algeria,
2002.
@article{"International Journal of Medical, Medicine and Health Sciences:61197", author = "Ali Hussian Ali AlTimemy and Fawzi M. Al Naima", title = "Comparison of Different Neural Network Approaches for the Prediction of Kidney Dysfunction", abstract = "This paper presents the prediction of kidney
dysfunction using different neural network (NN) approaches. Self
organization Maps (SOM), Probabilistic Neural Network (PNN) and
Multi Layer Perceptron Neural Network (MLPNN) trained with Back
Propagation Algorithm (BPA) are used in this study. Six hundred and
sixty three sets of analytical laboratory tests have been collected from
one of the private clinical laboratories in Baghdad. For each subject,
Serum urea and Serum creatinin levels have been analyzed and tested
by using clinical laboratory measurements. The collected urea and
cretinine levels are then used as inputs to the three NN models in
which the training process is done by different neural approaches.
SOM which is a class of unsupervised network whereas PNN and
BPNN are considered as class of supervised networks. These
networks are used as a classifier to predict whether kidney is normal
or it will have a dysfunction. The accuracy of prediction, sensitivity
and specificity were found for each type of the proposed networks
.We conclude that PNN gives faster and more accurate prediction of
kidney dysfunction and it works as promising tool for predicting of
routine kidney dysfunction from the clinical laboratory data.", keywords = "Kidney Dysfunction, Prediction, SOM, PNN,BPNN, Urea and Creatinine levels.", volume = "5", number = "4", pages = "163-7", }
