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A current screening guideline for youth diabetes risk (preDM/DM) shows poor performance. Machine learning models offer a promising alternative for accurate identification of prediabetes and diabetes mellitus in young people.

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Area of Science:

  • Pediatrics
  • Endocrinology
  • Public Health

Background:

  • Youth prediabetes and diabetes mellitus (preDM/DM) prevalence is rising.
  • Existing screening tools are designed for adults, not youth.
  • Reliable screening is crucial for early intervention in pediatric populations.

Purpose of the Study:

  • To evaluate a published pediatric clinical screening guideline's effectiveness in identifying youth with preDM/DM.
  • To compare the guideline's performance against American Diabetes Association biomarker criteria.
  • To assess the utility of machine learning classifiers for youth diabetes risk screening.

Main Methods:

  • Utilized a large-scale National Health and Nutritional Examination Survey (NHANES) dataset.
  • Assessed guideline performance using sensitivity, specificity, predictive values, F-measure, and Kappa.
  • Developed and compared machine learning (ML) classifiers against the guideline and biomarkers.

Main Results:

  • 29% of 2858 youth studied had preDM/DM based on biomarkers.
  • The clinical guideline exhibited low sensitivity (43.1%) and moderate specificity (67.6%).
  • ML classifiers showed comparable or superior performance in identifying preDM/DM youth.

Conclusions:

  • The evaluated pediatric clinical screening guideline is not effective for identifying youth preDM/DM.
  • Further research is needed to develop accurate youth diabetes risk screeners.
  • Advanced ML methods and diverse health data show potential for improved pediatric screening tools.