Comparison of Different Neural Network Approaches for the Prediction of Kidney Dysfunction

Authors: Ali Hussian Ali AlTimemy, Fawzi M. Al Naima

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.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1078681

References:

[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.