You are in:Home/Publications/Fast neural network learning algorithms for medical applications. Neural Computing and Applications, 23(3-4): 1019-1034. Springer. DOI: 10.1007/s00521-012-1026-y [ISI Indexed: Impact Factor: 2.505].

Dr. Assoc. Prof. Ahmad Taher Azar :: Publications:

Title:
Fast neural network learning algorithms for medical applications. Neural Computing and Applications, 23(3-4): 1019-1034. Springer. DOI: 10.1007/s00521-012-1026-y [ISI Indexed: Impact Factor: 2.505].
Authors: Azar AT
Year: 2013
Keywords: Not Available
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Volume: Not Available
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Pages: Not Available
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Local/International: International
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Abstract:

Measuring the blood urea nitrogen concentration is crucial to evaluate dialysis dose (Kt/V) in patients with renal failure. Although frequent measurement is needed to avoid inadequate dialysis efficiency, artificial intelligence can repeatedly perform the forecasting tasks and may be a satisfactory substitute for laboratory tests. Artificial neural networks represent a promising alternative to classical statistical and mathematical methods to solve multidimensional nonlinear problems. It also represents a promising forecasting application in nephrology. In this study, multilayer perceptron (MLP) neural network with fast learning algorithms is used for the accurate prediction of the post-dialysis blood urea concentration. The capabilities of eight different learning algorithms are studied, and their performances are compared. These algorithms are Levenberg–Marquardt, resilient backpropagation, scaled conjugate gradient, conjugate gradient with Powell–Beale restarts, Polak–Ribiere conjugate gradient and Fletcher–Reeves conjugate gradient algorithms, BFGS quasi-Newton, and one-step secant. The results indicated that BFGS quasi-Newton and Levenberg–Marquardt algorithm produced the best results. Levenberg–Marquardt algorithm outperformed clearly all the other algorithms in the verification phase and was a very robust algorithm in terms of mean absolute error (MAE), root mean square error (RMSE), Pearson’s correlation coefficient ( R2p ) and concordance coefficient (R C ). The percentage of MAE and RMSE for Levenberg–Marquardt is 0.27 and 0.32 %, respectively, compared to 0.38 and 0.41 % for BFGS quasi-Newton and 0.44 and 0.48 % for resilient backpropagation. MLP-based systems can achieve satisfying results for predicting post-dialysis blood urea concentration and single-pool dialysis dose sp Kt/V without the need of a detailed description or formulation of the underlying process in contrast to most of the urea kinetic modeling techniques.

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