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:预测体脂可以为医务人员和用户提供预防和诊断心脏疾病的重要信息。混合机器学习模型通过选择相关身体测量指标和捕获所选特征之间的复杂非线性关系,在建模体脂预测问题上比简单的回归分析方法具有更好的性能。然而,它们也存在一些缺点。当前的机器学习模型将身体脂肪预测建模为组合的单目标和多目标优化问题,往往会卡在局部最优解中。当多个特征子集产生类似或接近的预测时,避免局部最优解变得更加复杂。进化特征选择已被用来解决几个基于机器学习的优化问题。模糊集理论确定适当的探索和开发水平,同时管理参数化和计算成本。本文探讨了一种使用进化特征选择、模糊集理论和机器学习算法的带加权和体脂预测方法,将矛盾指标集成到由模糊自适应进化特征选择优化的单一综合目标中。本文应用了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,构建了一个单目标特征选择机器学习框架(FAGLSUD-based FS-ML)。该模型使用较少的特征,在准确性和稳定性方面均优于其他混合和最先进的机器学习模型。我们还提出了一个多目标FAGLSUD-based FS-MLP,可以同时分析准确性、稳定性和维度冲突,为医务人员和用户提供更好的关于重要身体部位的脂肪含量和血脂水平的决策依据。