A fuzzy-weighted Gaussian kernel-based machine learning approach for body fat prediction

Obesity is a critical public health problem associated with various complications and diseases. Accurate prediction of body fat is crucial for diagnosing obesity. Various measurement methods, including underwater weighing, dual energy X-ray absorptiometry, bioelectrical impedance analysis, magnetic resonance imaging, air displacement plethysmography, and near infrared interactance, have been used to assess body fat. These measurement methods, however, require special equipment associated with high-cost tests. The aim of this study is to investigate the use of machine learning-based models to accurately predict the body fat percentage. Considering the fact that off-the-shelf machine learning-based models are typically sensitive to noise data, we propose a fuzzy-weighted Gaussian kernel-based Relative Error Support Vector Machine (RE-SVM) for body fat prediction. We first design a fuzzy-weighted operation, which applies fuzzy weights to the error constraints of the RE-SVM, to alleviate the influence of noise data. Next, we also apply the fuzzy weights to improve the Gaussian kernel by considering the importance of different samples. Computational experiments and statistical tests conducted confirm that our proposed approach is able to significantly outperform other models being compared for body fat prediction across different performance metrics used. The proposed approach offers a viable alternative for diagnosing obesity when high-cost measurement methods are not available.