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.

翻译：体脂预测可为医疗从业者和用户提供预防与诊断心脏疾病的关键信息。混合机器学习模型通过选择相关身体测量指标并捕捉选定特征间复杂的非线性关系，在体脂预测建模中展现出优于简单回归分析方法的性能。然而，现有方法仍存在局限性。当前将体脂预测建模为组合式单目标与多目标优化问题的机器学习方法常陷入局部最优解。当多个特征子集产生相似或接近的预测结果时，避免局部最优问题变得更为复杂。进化特征选择技术已应用于解决多种基于机器学习的优化问题。模糊集理论可在管理参数化与计算成本的同时，确定适当的探索与利用平衡度。本研究采用加权求和的体脂预测方法，结合进化特征选择、模糊集理论与机器学习算法，将相互矛盾的度量指标整合为通过模糊自适应进化特征选择优化的单一复合目标。基于模糊自适应全局学习局部搜索通用多样性的特征选择方法被应用于该单目标特征选择-机器学习框架（基于FAGLSUD的FS-ML）。该模型在使用更少特征的同时，相比其他混合及前沿机器学习模型实现了更精确、更稳定的体脂百分比估计。本文还提出了多目标基于FAGLSUD的FS-MLP框架，以同步分析精度、稳定性与维度冲突。医疗从业者和用户可通过分布均匀的帕累托折衷解集，对关键身体部位的脂肪堆积与血脂水平做出科学决策。