  Predicting body fat can provide medical practitioners and users with
essential information for preventing and diagnosing heart diseases. Hybrid
machine learning models offer better performance than simple regression
analysis methods by selecting relevant body measurements and capturing complex
nonlinear relationships among selected features in modelling body fat
prediction problems. There are, however, some disadvantages to them. Current
machine learning. Modelling body fat prediction as a combinatorial single- and
multi-objective optimisation problem often gets stuck in local optima. When
multiple feature subsets produce similar or close predictions, avoiding local
optima becomes more complex. Evolutionary feature selection has been used to
solve several machine-learning-based optimisation problems. A fuzzy set theory
determines appropriate levels of exploration and exploitation while managing
parameterisation and computational costs. A weighted-sum body fat prediction
approach was explored using evolutionary feature selection, fuzzy set theory,
and machine learning algorithms, integrating contradictory metrics into a
single composite goal optimised by fuzzy adaptive evolutionary feature
selection. Hybrid fuzzy adaptive global learning local search universal
diversity-based feature selection is applied to this single-objective feature
selection-machine learning framework (FAGLSUD-based FS-ML). While using fewer
features, this model achieved a more accurate and stable estimate of body fat
percentage than other hybrid and state-of-the-art machine learning models. A
multi-objective FAGLSUD-based FS-MLP is also proposed to analyse accuracy,
stability, and dimensionality conflicts simultaneously. To make informed
decisions about fat deposits in the most vital body parts and blood lipid
levels, medical practitioners and users can use a well-distributed Pareto set
of trade-off solutions.
