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Novel method to predict body weight in children based on age and morphological facial features
Author(s) -
Huang Ziyin,
Barrett Jeffrey S.,
Barrett Kyle,
Barrett Ryan,
Ng Chee M.
Publication year - 2015
Publication title -
the journal of clinical pharmacology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 116
eISSN - 1552-4604
pISSN - 0091-2700
DOI - 10.1002/jcph.422
Subject(s) - artificial intelligence , mean squared error , feature (linguistics) , mean absolute error , correlation coefficient , body weight , pattern recognition (psychology) , artificial neural network , correlation , computer science , statistics , mathematics , medicine , philosophy , linguistics , geometry
A new and novel approach of predicting the body weight of children based on age and morphological facial features using a three‐layer feed‐forward artificial neural network (ANN) model is reported. The model takes in four parameters, including age‐based CDC‐inferred median body weight and three facial feature distances measured from digital facial images. In this study, thirty‐nine volunteer subjects with age ranging from 6–18 years old and BW ranging from 18.6–96.4 kg were used for model development and validation. The final model has a mean prediction error of 0.48, a mean squared error of 18.43, and a coefficient of correlation of 0.94. The model shows significant improvement in prediction accuracy over several age‐based body weight prediction methods. Combining with a facial recognition algorithm that can detect, extract and measure the facial features used in this study, mobile applications that incorporate this body weight prediction method may be developed for clinical investigations where access to scales is limited.