Predicting youth diabetes risk using NHANES data and machine learning

Gaurav Pandey

Abstract:

Prediabetes and diabetes mellitus (preDM/DM) have become alarmingly prevalent among youth in
recent years. However, simple questionnaire-based screening tools to reliably assess diabetes risk
are only available for adults, not youth. As a frst step in developing such a tool, we used a large-scale
dataset from the National Health and Nutritional Examination Survey (NHANES) to examine the
performance of a published pediatric clinical screening guideline in identifying youth with preDM/
DM based on American Diabetes Association diagnostic biomarkers. We assessed the agreement
between the clinical guideline and biomarker criteria using established evaluation measures
(sensitivity, specifcity, positive/negative predictive value, F-measure for the positive/negative preDM/
DM classes, and Kappa). We also compared the performance of the guideline to those of machine
learning (ML) based preDM/DM classifers derived from the NHANES dataset. Approximately 29%
of the 2858 youth in our study population had preDM/DM based on biomarker criteria. The clinical
guideline had a sensitivity of 43.1% and specifcity of 67.6%, positive/negative predictive values of
35.2%/74.5%, positive/negative F-measures of 38.8%/70.9%, and Kappa of 0.1 (95%CI: 0.06–0.14).
The performance of the guideline varied across demographic subgroups. Some ML-based classifers
performed comparably to or better than the screening guideline, especially in identifying preDM/DM
youth (p = 5.23 × ­10−5).We demonstrated that a recommended pediatric clinical screening guideline did
not perform well in identifying preDM/DM status among youth. Additional work is needed to develop
a simple yet accurate screener for youth diabetes risk, potentially by using advanced ML methods and
a wider range of clinical and behavioral health data.