title: Applying an artificial neural network to predict total body water in 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 predict TBW, but a more accurate method is needed. We developed an artificial 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 five 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 significantly higher than TBW-BIA (31.341 +/- 6.033 liters). The only non-significant difference was between TBW-ANN (31.468 +/- 5.301 liters) and TBW-BIA (p = 0.639). ANN had the strongest Pearson's correlation coefficient (0.911) and smallest root mean square error (2.480); its peak centered most closely to zero with the shortest tails in an empirical cumulative distribution plot when compared with the other five equations.
  
  
  CONCLUSIONS
  ANN could surpass traditional anthropometric equations and serve as a feasible alternative method of TBW estimation for chronic hemodialysis patients.
authors.affiliation: Department of Nuclear Medicine, Buddhist Dalin Tzu Chi General Hospital, Chiayi County, Taiwan.
authors.forename: Jainn-Shiun
authors.initials: JS
authors.lastname: Chiu
authors.forename: Chee-Fah
authors.initials: CF
authors.lastname: Chong
authors.forename: Yuh-Feng
authors.initials: YF
authors.lastname: Lin
authors.forename: Chia-Chao
authors.initials: CC
authors.lastname: Wu
authors.forename: Yuh-Feng
authors.initials: YF
authors.lastname: Wang
authors.forename: Yu-Chuan
authors.initials: YC
authors.lastname: Li
mesh_terms: D000328:Adult; D000368:Aged; D000886:Anthropometry / Q000379:methods; D001834:Body Water / Q000378:metabolism*; D017097:Electric Impedance; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D006435:Renal Dialysis