Background The number of children with obesity has increased in Saudi Arabia, which is a significant public health concern. Early diagnosis of childhood obesity and screening of the prevalence is needed using a simple in situ method. This study aims to generate statistical equations to predict body fat percentage (BF%) for Saudi children by employing machine learning technology and to establish gender and age-specific body fat reference range. Methods Data was combined from two cross-sectional studies conducted in Saudi Arabia for 1,292 boys and girls aged 8–12 years. Body fat was measured in both studies using bio-electrical impedance analysis devices. Height and weight were measured and body mass index was calculated and classified according to CDC 2,000 charts. A total of 603 girls and 374 boys were randomly selected for the learning phase, and 153 girls and 93 boys were employed in the validation set. Analyses of different machine learning methods showed that an accurate, sensitive model could be created. Two regression models were trained and fitted with the construction samples and validated. Gradient boosting algorithm was employed to achieve a better estimation and produce the equations, then the root means squared error (RMSE) equation was performed to decrease the error. Body fat reference ranges were derived for children aged 8–12 years. Results For the gradient boosting models, the predicted fat percentage values were more aligned with the true value than those in regression models. Gradient boosting achieved better performance than the regression equation as it combined multiple simple models into a single composite model to take advantage of that weak classifier. The developed predictive model archived RMSE of 3.12 for girls and 2.48 boys. BF% and Fat mass index charts were presented in which cut-offs for 5th, 75th and 95th centiles are used to define ‘under-fat’, ‘normal’, ‘overfat’ and ‘subject with obesity’. Conclusion Machine learning models could represent a significant advancement for investigators studying adiposity-related issues in children. These models and newly developed centile charts could be useful tools for the estimation and classification of BF%.

中文翻译：

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使用机器技术方法的沙特阿拉伯 8-12 岁儿童的脂肪量预测方程和参考范围

背景 在沙特阿拉伯，肥胖儿童的数量有所增加，这是一个重大的公共卫生问题。需要使用简单的原位方法对儿童肥胖症进行早期诊断和筛查患病率。本研究旨在通过采用机器学习技术生成统计方程来预测沙特儿童的体脂百分比 (BF%)，并建立针对性别和年龄的体脂参考范围。方法 数据来自于沙特阿拉伯进行的两项横断面研究，涉及 1,292 名 8-12 岁的男孩和女孩。两项研究均使用生物电阻抗分析设备测量体脂。测量身高和体重，并根据 CDC 2,000 图表计算和分类体重指数。学习阶段随机选择了603名女孩和374名男孩，验证集中雇用了 153 名女孩和 93 名男孩。对不同机器学习方法的分析表明，可以创建一个准确、敏感的模型。对两个回归模型进行了训练，并与构建样本进行了拟合并进行了验证。采用梯度提升算法来实现更好的估计并生成方程，然后执行均方根误差 (RMSE) 方程以减小误差。体脂参考范围是针对 8-12 岁儿童得出的。结果对于梯度增强模型，预测的脂肪百分比值比回归模型更符合真实值。梯度提升比回归方程实现了更好的性能，因为它将多个简单模型组合成一个复合模型以利用该弱分类器。开发的预测模型对女孩和男孩的 RMSE 归档为 3.12 和 2.48。提供了 BF% 和脂肪质量指数图表，其中第 5、第 75 和第 95 个百分位数的截止值用于定义“脂肪不足”、“正常”、“过胖”和“肥胖受试者”。结论 机器学习模型可以代表研究儿童肥胖相关问题的研究人员的重大进步。这些模型和新开发的百分位图可能是 BF% 估计和分类的有用工具。结论 机器学习模型可以代表研究儿童肥胖相关问题的研究人员的重大进步。这些模型和新开发的百分位图可能是 BF% 估计和分类的有用工具。结论 机器学习模型可以代表研究儿童肥胖相关问题的研究人员的重大进步。这些模型和新开发的百分位图可能是 BF% 估计和分类的有用工具。