Childhood and adolescent obesity rates are a global concern because obesity is associated with chronic diseases and long-term health risks. Artificial intelligence technology has emerged as a promising solution to accurately predict obesity rates and provide personalized feedback to adolescents. This study emphasizes the importance of early identification and prevention of obesity-related health issues. Factors such as height, weight, waist circumference, calorie intake, physical activity levels, and other relevant health information need to be considered for developing robust algorithms for obesity rate prediction and delivering personalized feedback. Hence, by collecting health datasets from 321 adolescents, we proposed an adolescent obesity prediction system that provides personalized predictions and assists individuals in making informed health decisions. Our proposed deep learning framework, DeepHealthNet, effectively trains the model using data augmentation techniques, even when daily health data are limited, resulting in improved prediction accuracy (acc: 0.8842). Additionally, the study revealed variations in the prediction of the obesity rate between boys (acc: 0.9320) and girls (acc: 0.9163), allowing the identification of disparities and the determination of the optimal time to provide feedback. The proposed system shows significant potential in effectively addressing childhood and adolescent obesity.

翻译：儿童和青少年肥胖率是全球关注的焦点，因为肥胖与慢性疾病和长期健康风险相关。人工智能技术已成为准确预测肥胖率并为青少年提供个性化反馈的前景解决方案。本研究强调早期识别和预防肥胖相关健康问题的重要性。为开发稳健的肥胖率预测算法并提供个性化反馈，需综合考虑身高、体重、腰围、热量摄入、身体活动水平及其他相关健康信息等因素。因此，通过收集321名青少年的健康数据集，我们提出了一种青少年肥胖预测系统，该系统可提供个性化预测并协助个人做出明智的健康决策。我们提出的深度学习框架DeepHealthNet，即使在日常健康数据有限的情况下，也能通过数据增强技术有效训练模型，从而提升预测准确率（acc: 0.8842）。此外，研究揭示了男孩（acc: 0.9320）和女孩（acc: 0.9163）在肥胖率预测上的差异，有助于识别性别差异并确定提供反馈的最佳时机。该提出的系统在有效应对儿童和青少年肥胖问题上展现出显著潜力。