Estimating youth diabetes risk using NHANES data and machine learning

Abstract

Type 2 diabetes has become alarmingly prevalent among youth in recent years. However, simple questionnaire-based screening tools to reliably identify diabetes risk and prevent the adverse effects of this serious disease are only available for adults, not for youth. As a first 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 well-known adult diabetes risk self-assessment screener and published pediatric clinical screening guidelines in identifying youth with pre-diabetes/diabetes (pre-DM/DM) based on American Diabetes Association diagnostic biomarkers. We assessed the agreement between the adult screener/pediatric screening guidelines and biomarker diagnostic criteria by conducting comparisons using the overall data set and sub-datasets stratified by sex, race/ethnicity, and age. While the pediatric guidelines performed better than the adult screener in identifying youth with pre-DM/DM (sensitivity 43.1% vs 7.2%), both are inadequate for general deployment among youth. There were also notable differences in the performance of the pediatric guidelines across subgroups based on age, sex and race/ethnicity. In an effort to improve pre-DM/DM screening, we also evaluated data-driven machine learning-based classification algorithms, several of which performed slightly but statistically significantly better than the pediatric screening guidelines.

Machine learning algorithms identify demographics, dietary features, and blood biomarkers associated with stroke records

“…Cross-sectional studies are helpful in planning public health interventions [6]. Many studies [7][8][9][10][11][12][13] have taken advantage of the NHANES to explore cardiovascular diseases, diabetes mellitus, and all-cause mortality. In contrast, few have used a comprehensive comparison of features for stroke prevalence.…”