Comput Methods Programs Biomed - Adaptive network based on fuzzy inference system for equilibrated urea concentration prediction.

Tópicos

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Resumo

Post-dialysis urea rebound (PDUR) has been attributed mostly to redistribution of urea from different compartments, which is determined by variations in regional blood flows and transcellular urea mass transfer coefficients. PDUR occurs after 30-90min of short or standard hemodialysis (HD) sessions and after 60min in long 8-h HD sessions, which is inconvenient. This paper presents adaptive network based on fuzzy inference system (ANFIS) for predicting intradialytic (Cint) and post-dialysis urea concentrations (Cpost) in order to predict the equilibrated (Ceq) urea concentrations without any blood sampling from dialysis patients. The accuracy of the developed system was prospectively compared with other traditional methods for predicting equilibrated urea (Ceq), post dialysis urea rebound (PDUR) and equilibrated dialysis dose (eKt/V). This comparison is done based on root mean squares error (RMSE), normalized mean square error (NRMSE), and mean absolute percentage error (MAPE). The ANFIS predictor for Ceq achieved mean RMSE values of 0.3654 and 0.4920 for training and testing, respectively. The statistical analysis demonstrated that there is no statistically significant difference found between the predicted and the measured values. The percentage of MAE and RMSE for testing phase is 0.63% and 0.96%, respectively.

Resumo Limpo

postdialysi urea rebound pdur attribut most redistribut urea differ compart determin variat region blood flow transcellular urea mass transfer coeffici pdur occur min short standard hemodialysi hd session min long h hd session inconveni paper present adapt network base fuzzi infer system anfi predict intradialyt cint postdialysi urea concentr cpost order predict equilibr ceq urea concentr without blood sampl dialysi patient accuraci develop system prospect compar tradit method predict equilibr urea ceq post dialysi urea rebound pdur equilibr dialysi dose ektv comparison done base root mean squar error rmse normal mean squar error nrmse mean absolut percentag error mape anfi predictor ceq achiev mean rmse valu train test respect statist analysi demonstr statist signific differ found predict measur valu percentag mae rmse test phase respect

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