Azar A.T.Department of Electrical Communication and ElectronicsSystems EngineeringModern Science and Arts University (MSA)26 July Mehwar Road Intersection with Wahat Road6th of October CityEgypt2020-01-252020-01-25201117408865https://doi.org/10.1504/IJISTA.2011.040352https://cutt.ly/Ar1oRmTScopusTotal dialysis dose (Kt/V) is considered to be a major determinant of morbidity and mortality in haemodialysed patients. The continuous growth of the blood urea concentration over the 30-60-min period following dialysis, a phenomenon known as urea rebound, is a critical factor in determining the true dose of haemodialysis (HD). The misestimation of the equilibrated (true) postdialysis blood urea or equilibrated Kt/V results in an inadequate HD prescription, with predictably poor clinical outcomes for the patients. The estimation of the equilibrated post-dialysis blood urea (C eq) is therefore crucial in order to estimate the equilibrated (true) Kt/V. Measuring post-dialysis urea rebound (PDUR) requires a 30- or 60-min post-dialysis sampling, which is inconvenient. This paper presents a novel technique for predicting equilibrated urea concentration and PDUR in the form of a Takagi-Sugeno-Kang fuzzy inference system. The advantage of this neuro-fuzzy hybrid approach is that it does not require 30-60-min post-dialysis urea sample. Adaptive neuro-fuzzy inference system (ANFIS) was constructed to predict equilibrated urea (C eq) taken at 60 min after the end of the HD session in order to predict PDUR. The accuracy of the ANFIS was prospectively compared with other traditional methods for predicting equilibrated urea (C eq), PDUR and equilibrated dialysis dose ( eqKt/V). The results are highly promising, and a comparative analysis suggests that the proposed modelling approach outperforms other traditional urea kinetic models. � 2011 Inderscience Enterprises Ltd.EnglisheqKt/VAdaptive neuro-fuzzy inference systemANFISC eqEquilibrated dialysis adequacyEquilibrated urea concentrationNeural networksPDURPost-dialysis urea reboundTakagi-Sugeno-Kang fuzzy inference systemArticlehttps://doi.org/10.1504/IJISTA.2011.040352