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：预测体脂可为医疗从业者和用户提供预防及诊断心脏疾病的关键信息。相较于简单回归分析方法，混合机器学习模型通过筛选相关身体测量指标并捕捉所选特征间的复杂非线性关系，在体脂预测建模中展现出更优性能。然而当前方法存在局限性：将体脂预测建模为组合单目标与多目标优化问题时，模型易陷入局部最优解；当多个特征子集产生相似或近似预测结果时，摆脱局部最优的难度进一步增加。进化特征选择已被成功应用于多种基于机器学习的优化问题。模糊集理论可确定探索与开发过程的适宜平衡度，同时有效管理参数化与计算成本。本研究采用加权求和体脂预测方法，融合进化特征选择、模糊集理论与机器学习算法，将矛盾性指标整合为单一复合目标，通过模糊自适应进化特征选择实现优化。将混合模糊自适应全局学习局部搜索通用多样性特征选择方法应用于该单目标特征选择-机器学习框架（FAGLSUD-based FS-ML）。相较于其他混合模型及当前最优机器学习模型，本模型在采用更少特征的前提下，实现了更精准稳定的体脂率估计。此外，本文提出多目标FAGLSUD-based FS-MLP模型，可同步分析准确性、稳定性与维度间的冲突。医疗从业者与用户可通过分布均匀的帕累托权衡解集，就关键身体部位的脂肪沉积及血脂水平做出科学决策。