Title: Adaptive modelling of creatinine concentration in human blood

Authors: Mashhour Bani Amer

Addresses: Faculty of Engineering, Department of Biomedical Engineering, Jordan University of Science and Technology, P.O. Box 3030, 22110 Irbid, Jordan

Abstract: The need and importance of modelling and evaluation of renal function has grown with the increasing need for kidney replacement, design of artificial kidney and proper diagnosis and treatment of kidney disorders. This paper explores a fuzzy model of the creatinine concentration (C_r) in human blood using adaptive Neurofuzzy Inference System (NFIS). This model aims to predict blood creatinine using blood urea, sodium and potassium without a need for actual measurement of C_r in blood. The creatinine level in blood is an efficient tool for evaluation and assessment of renal function. The proposed model was validated by comparing the predicted creatinine values from the model with the experimental creatinine values. The obtained results showed that the developed model is capable of estimating the blood creatinine with a Root-Mean Square Error (RMSE) of 0.76 and also the predicted C_r values agree closely with the experimentally measured C_r values. Practically, this implies that the developed neurofuzzy model is adequate one for prediction of creatinine concentration in human blood and consequently will lead to a proper evaluation of renal function.

Keywords: adaptive modelling; renal function evaluation; creatinine concentration; human blood; kidney replacement; artificial kidneys; kidney disorders; fuzzy models; fuzzy logic; neural networks; neurofuzzy inference systems; NFIS; blood urea; sodium; potassium.

DOI: 10.1504/IJBET.2011.043173

International Journal of Biomedical Engineering and Technology, 2011 Vol.7 No.2, pp.105 - 115

Published online: 21 Jan 2015 *

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