| Title: | Adaptive network based on fuzzy inference system for equilibrated urea concentration prediction. Computer Methods and Programs in Biomedicine, 111(3): 578–591, Elsevier. [ISI Indexed: Impact Factor: 2.503]. |
| Authors: | Azar AT |
| Year: | 2013 |
| Keywords: | Not Available |
| Journal: | Not Available |
| Volume: | Not Available |
| Issue: | Not Available |
| Pages: | Not Available |
| Publisher: | Not Available |
| Local/International: | International |
| Paper Link: | |
| Full paper | Not Available |
| Supplementary materials | Not Available |
| Abstract: |
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 30–90 min of short or standard hemodialysis (HD) sessions and after 60 min 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. |
