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September 10, 2025PLoS ONEOpen Access

AI-driven analysis of diabetes risk determinants in U.S. adults: Exploring disease prevalence and health factors

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Authors

DMDawid MajcherekACAdam CiesielskiPSPaweł Sobczak

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Overview

This study demonstrates high predictive accuracy for diabetes risk in U.S. adults using machine learning, highlighting key risk factors.

Key Points

  • The Extra Trees Classifier achieved the highest predictive accuracy, with over 90% and an AUC of 0.99.
  • Analysis identified BMI, age, income, and general health status as major predictors of diabetes risk.
  • Datasets from the 2015 Behavioral Risk Factor Surveillance System included 253,680 adult respondents with diverse health behaviors.
  • Tree-based ensemble machine learning methods may enhance public health tools for targeted diabetes risk assessment.

Cite This Study

Majcherek et al. (2025) studied this question.

synapsesocial.com/papers/68c23b8fb210217d647857c7https://doi.org/10.1371/journal.pone.0328655
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