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一个全球关注的问题，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精准预测肥胖率并为青少年提供个性化反馈的有效解决方案。本研究强调了早期识别和预防肥胖相关健康问题的重要性。构建稳健的肥胖率预测算法并提供个性化反馈，需要考虑身高、体重、腰围、卡路里摄入量、体力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，并帮助个体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，从而提高预测精度（准确率：0.8842）。此外，研究揭示了男孩（准确率：0.9320）和女孩（准确率：0.9163）在肥胖率预测上的差异，从而能够识别差异并确定提供反馈的最佳时机。所提出的系统在有效应对儿童和青少年肥胖问题方面显示出巨大潜力。