Applying an Artificial Neural Network to Predict Total Body Water in Hemodialysis Patients

Jainn-Shiun Chiu, Chee-Fah Chong, Yuh-Feng Lin, Chia-Chao Wu, Yuh-Feng Wang, Yu-Chuan Li
2005 American Journal of Nephrology  
centered most closely to zero with the shortest tails in an empirical cumulative distribution plot when compared with the other fi ve equations. Conclusion: ANN could surpass traditional anthropometric equations and serve as a feasible alternative method of TBW estimation for chronic hemodialysis patients. Abstract Background: Estimating total body water (TBW) is crucial in determining dry weight and dialytic dose for hemodialysis patients. Several anthropometric equations have been used to
more » ... ve been used to predict TBW, but a more accurate method is needed. We developed an artifi cial neural network (ANN) to predict TBW in hemodialysis patients. Methods: Demographic data, anthropometric measurements, and multifrequency bioelectrical impedance analysis (MF-BIA) were investigated in 54 patients. TBW measured by MF-BIA (TBW-BIA) was the reference. The predictive value of TBW based on ANN and fi ve anthropometric equations (58% of actual body weight, Watson formula, Hume formula, Chertow formula, and Lee formula) was evaluated. Results: Predictive TBW values derived from anthropometric equations were signifi cantly higher than TBW-BIA (31.341 8 6.033 liters). The only non-signifi cant difference was between TBW-ANN (31.468 8 5.301 liters) and TBW-BIA (p = 0.639). ANN had the strongest Pearson's correlation coeffi cient (0.911) and smallest root mean square error (2.480); its peak
doi:10.1159/000088279 pmid:16155360 fatcat:yvyujwbjongwhclfel3d5hfo6a