Employing Unsupervised Machine Learning Techniques to Analyze Cardio-Metabolic Profiles in Middle-Aged and Older Chinese Individuals with General and Central Obesity - Top-Institutions - MDSpire

Employing Unsupervised Machine Learning Techniques to Analyze Cardio-Metabolic Profiles in Middle-Aged and Older Chinese Individuals with General and Central Obesity

  • By

  • Yan Xue

  • Menghuan Song

  • Carolina Oi Lam Ung

  • Hao Hu

  • October 27, 2025

  • 0 min

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Top Institutions in Endocrinology

Brief introduction explaining scope and methodology.

  • #1

    Harvard T.H. Chan School of Public Health
    Harvard T.H. Chan School of Public Health

    Boston, MA

    Key Differentiators

    • Epidemiology
    • Cardiometabolic Disease
    • Machine Learning in Medicine

  • #2

    Johns Hopkins University School of Medicine
    Johns Hopkins University School of Medicine

    Baltimore, MD

    Key Differentiators

    • Cardiology
    • Endocrinology
    • Biomedical Data Science

  • #3

    University of Oxford
    University of Oxford

    Oxford, UK

    Key Differentiators

    • Population Health
    • Machine Learning
    • Metabolic Disease

  • #4

    Peking University Health Science Center
    Peking University Health Science Center

    Beijing, China

    Key Differentiators

    • Public Health
    • Endocrinology
    • Data Science

  • #5

    Stanford University School of Medicine
    Stanford University School of Medicine

    Stanford, CA

    Key Differentiators

    • Biomedical Informatics
    • Endocrinology
    • Machine Learning

Original Source(s)

Employing Unsupervised Machine Learning Techniques to Analyze Cardio-Metabolic Profiles in Middle-Aged and Older Chinese Individuals with General and Central Obesity

  • by Yan Xue, Menghuan Song, Carolina Oi Lam Ung, Hao Hu

  • October 27, 2025

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