Authors: Ahmad Taher Azar
Addresses: Scientific Computing Department, Faculty of Computers and Information, Benha University, Benha, Qalyubia 13511, Egypt
Abstract: Dialysis dose (Kt/V) is mostly dependent on dialysis kinetic variables such as pre-dialysis and post-dialysis blood urea nitrogen concentration (Cpost), ultrafiltration (UF) volume, duration of the dialysis procedure, and urea distribution volume. Therefore, post-dialysis blood urea concentration is used to assess the dialysis efficiency. It gradually decreases to about 30% of the pre-dialysis value depending on the urea clearance rate during the period of dialysis. If the urea removal is inadequate, then dialysis is inadequate. This paper proposes a novel method, Adaptive Neuro-Fuzzy Inference System (ANFIS) to predict the post-dialysis blood urea concentration. The advantage of this neuro-fuzzy hybrid approach is that it does not require the model structure to be known a priori, in contrast to most of the urea kinetic modelling techniques. The accuracy of the ANFIS was prospectively compared with other traditional methods for predicting single pool dialysis dose (spKt/V). The results are highly promising, and a comparative analysis suggests that the proposed modelling approach outperforms other traditional urea kinetic models (UKM).
Keywords: artificial neural networks; Takagi-Sugeno-Kang fuzzy inference system; ANFIS; adaptive neurofuzzy inference system; feature selection; single-pool kinetic modelling; post-dialysis urea concentration; blood urea concentration; dialysis efficiency; nephrology; kidney disease.
International Journal of Intelligent Systems Technologies and Applications, 2013 Vol.12 No.2, pp.87 - 110
Received: 08 Dec 2011
Accepted: 07 Mar 2012
Published online: 31 Aug 2013 *