# Development of a Simple Youth Diabetes Screening Tool Using Machine Learning

> **NIH NIH R21** · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · 2022 · $253,500

## Abstract

Project Summary
 The number of youth with type 2 diabetes in the U.S. is projected to increase by a staggering 49
percent by 2050. However, simple screening tools to reliably identify diabetes risk and prevent the adverse
effects of this serious disease are only available for adults, not for youth. Indeed, our preliminary studies using
nationally representative data from the National Health and Nutrition Examination Survey (NHANES) found that
published pediatric clinical guidelines performed relatively poorly in capturing youth with diabetes or its
precursor condition (prediabetes). In response to this urgent health challenge, this R21 research proposal aims
to bring together clinical, epidemiology and data science experts to develop and validate a youth diabetes risk
screener. We will develop a user-friendly screening tool to identify youth with prediabetes or diabetes by
leveraging state of the art machine learning techniques and rich data from NHANES. Our final product will be a
web-based screener that can be integrated into both digital health platforms and traditional public health
surveillance efforts. This translational product will aid parents, community-based organizations, schools, and
primary care providers in accurately identifying youth at risk of diabetes who can benefit from subsequent
definitive diagnostic testing, as well as prevention and medical management programs.
Specific Aims: 1. We will develop an initial candidate screener to distinguish between normal and
prediabetic/diabetic youth by applying parsimonious predictive modeling-oriented machine learning techniques
to NHANES data. 2. We will develop additional candidate screeners that integrate the various domains of
NHANES data, and will identify the best-performing screener for youth by comparing the performances of all
the candidates. 3. To address the importance of sociodemographic factors for prediabetes/diabetes screening,
we will also develop candidate screeners specific to sociodemographic subgroups based on age, sex and
race/ethnicity. We will validate all these candidates to develop an integrated screener that is accurate and
personalized to individuals based on their sociodemographic characteristics (age, sex and race/ethnicity).
Finally, we will implement a user-friendly web-based version of the integrated screener than can help identify
youth at risk of diabetes, and become part of a community-based youth diabetes prevention strategy for future
implementation in high-risk communities.

## Key facts

- **NIH application ID:** 10354792
- **Project number:** 1R21DK131555-01
- **Recipient organization:** ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
- **Principal Investigator:** Bian Liu
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $253,500
- **Award type:** 1
- **Project period:** 2022-04-01 → 2024-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10354792

## Citation

> US National Institutes of Health, RePORTER application 10354792, Development of a Simple Youth Diabetes Screening Tool Using Machine Learning (1R21DK131555-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10354792. Licensed CC0.

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