Artificial neural network models for accurate predictions of fat-free and fat masses, using easy-to-measure anthropometric parameters

HIGHLIGHTS

SUMMARY

Research in this field showed that multivariate regression models, receiver operating characteristic curves and neural_networks are methods implemented for the prediction of body composition. The aim of the study is to develop accurate algorithms that predict body masses, and specifically trunk fat and fat-free masses, from easy-to-measure parameters in any setting. To validate the best approach, the authors aim to compare linear regression models with neural_networks that can capture non-linear relationships between variables. To achieve the best performance, a comparison was performed between multiple linear regression (MLR) and artificial neural_network . . .

 



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