Multi-adaptive neural-fuzzy system as a novel predictor of in-vivo blood side dialyser urea clearance
by Ahmad Taher Azar
International Journal of Computer Applications in Technology (IJCAT), Vol. 46, No. 2, 2013

Abstract: The blood urea concentration has been used as a surrogate marker for toxin elimination in hemodialysed patients, and several indices based on it have been proposed in recent years for monitoring treatment adequacy. Measuring urea nitrogen concentrations at the inlet and outlet of dialyser is crucial to evaluate the in-vivo blood side dialyser urea clearance during hemodialysis. 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. Neuro-fuzzy technology represents a promising forecasting application in clinical medicine. In this study, two fuzzy models have been proposed to predict dialyser inlet and outlet urea concentrations in order to estimate dialyser clearance without blood sampling. The model is of multi-input single-output (MISO) type. Multi-adaptive neuro-fuzzy inference system (MANFIS) technique of fuzzy-based systems has been employed. The performance of the model is authenticated by evaluating the predicted results with the practical results obtained by conducting the confirmation experiments. The results suggest that the neuro-fuzzy technology, based on limited clinical parameters, is an excellent alternative method for accurately predicting arterial and venous urea concentrations in hemodialysis patients. The proposed model can be used for intelligent online adaptive system.

Online publication date: Wed, 29-May-2013

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