用可穿戴设备和AI预测减肥效果，准确率达84.44%
Personalized Weight Loss Management through Wearable Devices and Artificial Intelligence
- 融合生理、行为数据，用梯度提升模型识别有效减重人群
- 84.44%的AUC表现，验证多源数据融合的有效性
- 适合个性化健康管理与慢性病早期干预研究者参考
早期发现慢性非传染性疾病对治疗至关重要。本研究探索可穿戴设备与人工智能在预测超重及肥胖人群体重变化中的应用。基于AI4FoodDB数据库中约100名受试者为期一个月的试验数据，涵盖生物标志物、生命体征与行为数据，分析达成至少2%初始体重减轻者与未达标者的差异。通过特征选择与分类算法，梯度提升分类器取得84.44%的曲线下面积（AUC）表现。多源数据（如生命体征、体力活动、睡眠活动等）融合显著提升预测性能，表明可穿戴设备与AI在个性化医疗中的巨大潜力。
原文摘要 · Abstract (English)
Early detection of chronic and Non-Communicable Diseases (NCDs) is crucial for effective treatment during the initial stages. This study explores the application of wearable devices and Artificial Intelligence (AI) in order to predict weight loss changes in overweight and obese individuals. Using wearable data from a 1-month trial involving around 100 subjects from the AI4FoodDB database, including biomarkers, vital signs, and behavioral data, we identify key differences between those achieving weight loss (>= 2% of their initial weight) and those who do not. Feature selection techniques and classification algorithms reveal promising results, with the Gradient Boosting classifier achieving 84.44% Area Under the Curve (AUC). The integration of multiple data sources (e.g., vital signs, physical and sleep activity, etc.) enhances performance, suggesting the potential of wearable devices and AI in personalized healthcare.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。