Predicting the intracellular water compartment using artificial neural network analysis

E. I. Mohamed*, C. Maiolo, R. Linder, S. J. Pöppl, A. De Lorenzo

*Corresponding author for this work
2 Citations (Scopus)


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 40K counting (ICW K40), were measured for all study participants. The anthropometric variables and the impedance index (height2/resistance) were fed to the ANN input layer, which produced as output the estimated values for intracellular water (ICWANN). We also estimated intracellular water using a BIA formula for the same population (ICWDeLorenzo) and another for Caucasians (ICWGudivaka). 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 1, whereas the mean estimated value was 15.20±1.79 1 (RMSE=11.06 1) when calculated using ICWDeLorenzo, 18.07±1.14 1 (RMSE=8.72 1) when using ICWGudivaka, and 25.01±2.74 1 (RMSE=3.22 1) when using ICWANN. Based on these results, we deduce that the ANN algorithm is a more accurate predictor for reference ICW K40 than BIA equations.

Original languageEnglish
JournalActa Diabetologica
Issue numberSUPPL. 1
Publication statusPublished - 01.10.2003


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