Background and Objective: The term ‘obesity’ refers to excessive body fat, and it is a chronic disease associated with various complications. Although a range of techniques for body fat estimation have been developed to assess obesity, they are typically associated with high-cost tests requiring special equipment. Accurate prediction of the body fat percentage based on easily accessed body measurements is thus important for assessing obesity and its related diseases. This paper presents an improved relative error support vector machine approach to predict body fat in a cost-effective manner.

Methods: Our proposed method introduces a bias error control term into its objective function to obtain an unbiased estimation. Feature selection is also utilised, by removing either redundant or irrelevant features without incurring much loss of information, to further improve the prediction accuracy. In addition, the Wilcoxon rank-sum test is used to validate if the performance of our proposed method is significantly better than other prediction models being compared.

Results: Experimental results based on four evaluation metrics show that the proposed method is able to outperform other prediction models under comparison. Considering the characteristics of different features (e.g., body measurements), we show that applying feature selection can further improve the prediction performance. Statistical analysis carried out confirms that our proposed method has obtained significantly better results than other compared methods.

Conclusions: We have proposed a new approach to predict the body fat percentage effectively. This approach can provide a good reference for people to know their body fat percentage with easily accessed measurements. Statistical test results based on the Wilcoxon rank-sum test not only show that our proposed method has significantly better performance than other prediction models being compared, but also confirm the usefulness of incorporating feature selection into the proposed method.

背景与目的： “肥胖”一词是指体内过多的脂肪，是一种与各种并发症相关的慢性疾病。尽管已开发出多种评估人体脂肪的技术来评估肥胖症，但它们通常与需要特殊设备的高成本测试有关。因此，基于易于获取的身体测量结果准确预测身体脂肪百分比对于评估肥胖症及其相关疾病非常重要。本文提出了一种改进的相对误差支持向量机方法，以一种经济有效的方式预测体内脂肪。

方法：我们提出的方法在其目标函数中引入了偏差误差控制项，以获得无偏估计。通过去除冗余或不相关的特征而又不造成太多信息损失的特征选择也被利用，以进一步提高预测精度。此外，Wilcoxon秩和检验用于验证我们提出的方法的性能是否明显优于其他比较的预测模型。

结果：基于四个评估指标的实验结果表明，该方法能够胜过其他比较模型。考虑到不同特征的特征（例如，身体测量），我们表明应用特征选择可以进一步提高预测性能。进行的统计分析证实，我们提出的方法比其他比较方法获得了明显更好的结果。

结论：我们提出了一种新的方法来有效地预测体内脂肪百分比。这种方法可以为人们通过易于访问的测量了解他们的体内脂肪百分比提供很好的参考。基于Wilcoxon秩和检验的统计测试结果不仅表明我们提出的方法比正在比较的其他预测模型具有明显更好的性能，而且证实了将特征选择合并到提出的方法中的有用性。