title: Predicting the intracellular water compartment using artificial neural network analysis.
abstract: Artificial neural networks (ANN) are used for a wide variety of data-processing applications such as predicting medical outcomes and classifying clinical data and patients. We investigated the applicability of an ANN for estimating the intracellular water compartment for a population of 104 healthy Italians ranging in age from 19 to 68 years. Anthropometric variables, bioelectric impedance analysis (BIA) variables, and reference values for intracellular water, measured using whole-body (40)K counting (ICW(K40)), were measured for all study participants. The anthropometric variables and the impedance index (height(2)/resistance) were fed to the ANN input layer, which produced as output the estimated values for intracellular water (ICW(ANN)). We also estimated intracellular water using a BIA formula for the same population (ICW(DeLorenzo)) and another for Caucasians (ICW(Gudivaka)). Errors in the estimations generated by ANN and the BIA equations were calculated as the root mean square error (RMSE). The mean (+/-SD) reference value (ICWK40) was 25.01+/-4.50 l, whereas the mean estimated value was 15.20+/-1.79 l (RMSE=11.06 l) when calculated using ICW(DeLorenzo), 18.07+/-1.14 l (RMSE=8.72 l) when using ICW(Gudivaka), and 25.01+/-2.74 l (RMSE=3.22 l) when using ICW(ANN). Based on these results, we deduce that the ANN algorithm is a more accurate predictor for reference ICW(K40) than BIA equations.
authors: Department of Neurosciences Building F-Sud, 1st Floor, Room 116, University of Tor Vergata, Via Montpellier 1, I-00133, Rome, Italy. eimohamed@med.uniroma2.it
authors: E I
authors: EI
authors: Mohamed
authors: C
authors: C
authors: Maiolo
authors: R
authors: R
authors: Linder
authors: S J
authors: SJ
authors: Pöppl
authors: A
authors: A
authors: De Lorenzo
mesh_terms: D000328:Adult; D000368:Aged; D001834:Body Water / Q000502:physiology*; D001835:Body Weight; D005260:Female; D006801:Humans; D042541:Intracellular Space / Q000502:physiology*; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D016571:Neural Networks, Computer; D012016:Reference Values