传统机器学习和深度学习算法在肥胖预测或分类中的应用：比较性能的系统综述
Applications of traditional machine learning and deep learning algorithms in obesity prediction or classification: a systematic review of comparative performance.
摘要(中文译文)
本系统综述评估了纵向研究中用于肥胖预测的传统机器学习（TML）和深度学习（DL）方法，以及横断面研究中用于肥胖分类的上述方法。检索了PubMed、Scopus和Web of Science，检索时间自建库至2025年6月15日。纳入应用这些算法并报告性能指标的研究。共有17项研究（3项纵向研究和14项横断面研究）符合纳入标准。17项纳入研究中仅1项进行了独立外部验证，校准报告极为有限，凸显了临床实施面临的重大方法学障碍。纵向研究显示出中等程度的性能，仅1项报告了区分度（AUC = 0.81），限制了对TML和DL在未来风险预测方面的比较。相比之下，横断面研究报告了较高的表观性能，DL模型的AUC最高达0.979，经过良好调优的TML模型也表现强劲；然而，许多估计值受到方法学局限性的影响，包括预测因子与结局之间的循环关系以及不完整的性能报告。两种方法在肥胖分类方面均表现良好，但关于具有临床意义的未来肥胖风险预测的证据仍然有限。需要纵向设计和独立外部验证以提高临床相关性。在获得此类证据之前，基于人工智能的肥胖模型应被视为探索性决策支持工具，而非可常规实施的临床或公共卫生风险评估工具。
Abstract
This systematic review evaluated traditional machine learning (TML) and deep learning (DL) approaches for obesity prediction in longitudinal studies and obesity classification in cross-sectional studies. PubMed, Scopus, and Web of Science were searched from inception to June 15, 2025. Studies applying these algorithms and reporting performance metrics were included. Seventeen studies (three longitudinal and fourteen cross-sectional) met the inclusion criteria. Only one of the 17 included studies performed independent external validation, and calibration reporting was extremely limited, highlighting substantial methodological barriers to clinical implementation. Longitudinal studies showed modest performance, with only one reporting discrimination (AUC = 0.81), limiting comparison between TML and DL for future risk prediction. In contrast, cross-sectional studies reported high apparent performance, with DL models reaching AUCs up to 0.979 and well-tuned TML models also performing strongly; however, many estimates were influenced by methodological limitations, including circular predictor-outcome relationships and incomplete performance reporting. Both approaches performed well for obesity classification, but evidence for clinically meaningful future obesity risk prediction remains limited. Longitudinal designs and independent external validation are needed to improve clinical relevance. Until such evidence is available, AI-based obesity models should be viewed as exploratory decision-support tools rather than routinely implemented instruments for clinical or public health risk assessment.
如何引用
AMA
Mehrdad Jamali, Meysam Zarezadeh, Mohammad Vesal Bideshki, Mohamed Khalifa, Michelle Cavaleri, Ahmad Saedisomeolia, et al. Applications of traditional machine learning and deep learning algorithms in obesity prediction or classification: a systematic review of comparative performance.. NPJ digital medicine. 2026; doi:10.1038/s41746-026-02986-8.
APA
Mehrdad Jamali, Meysam Zarezadeh, Mohammad Vesal Bideshki, Mohamed Khalifa, Michelle Cavaleri, Ahmad Saedisomeolia, et al (2026). Applications of traditional machine learning and deep learning algorithms in obesity prediction or classification: a systematic review of comparative performance.. NPJ digital medicine. https://doi.org/10.1038/s41746-026-02986-8
GB/T 7714
Mehrdad Jamali, Meysam Zarezadeh, Mohammad Vesal Bideshki, Mohamed Khalifa, Michelle Cavaleri, Ahmad Saedisomeolia, et al. Applications of traditional machine learning and deep learning algorithms in obesity prediction or classification: a systematic review of comparative performance.[J]. NPJ digital medicine, 2026 doi:10.1038/s41746-026-02986-8.
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