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September 10, 2025Archives of Medical ScienceOpen Access

Machine learning predicts diabetes risk in high-risk populations: based on the National Health and Nutrition Examination Survey database

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Authors

XYXiaohua YangMYMeiqi YaoJHJia Huang

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Overview

Analysis reveals machine learning predicts diabetes risk using National Health and Nutrition Examination Survey data, suggesting effective intervention measures.

Key Points

  • The random forest and XGBoost models achieved a higher AUC of 0.896 and 0.903, respectively, indicating strong predictive capabilities.
  • Out of 2,355 individuals analyzed, 260 cases of diabetes were identified, enhancing understanding of risk factors in high-risk populations.
  • Feature importance was primarily based on waist circumference, age, and BMI, emphasizing key risk factors in diabetes prediction.
  • Implementing these machine learning models may facilitate personalized treatment plans, significantly reducing diabetes incidence.

Cite This Study

Yang et al. (2025) studied this question.

synapsesocial.com/papers/68c23d3ab210217d6478cdf8https://doi.org/10.5114/aoms/209547
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