Estimation of total body fat using symbolic regression and evolutionary algorithms
Journal:
arXiv
Published Date:
Mar 1, 2025
Abstract
Body fat percentage is an increasingly popular alternative to Body Mass Index
to measure overweight and obesity, offering a more accurate representation of
body composition. In this work, we evaluate three evolutionary computation
techniques, Grammatical Evolution, Context-Free Grammar Genetic Programming,
and Dynamic Structured Grammatical Evolution, to derive an interpretable
mathematical expression to estimate the percentage of body fat that are also
accurate. Our primary objective is to obtain a model that balances accuracy
with explainability, making it useful for clinical and health applications. We
compare the performance of the three variants on a public anthropometric
dataset and compare the results obtained with the QLattice framework.
Experimental results show that grammatical evolution techniques can obtain
competitive results in performance and interpretability.
