A Deep Learning-Based Predictive Algorithm for Metabolic Syndrome Detection in the U.S. Population
Discuss this preprint
Start a discussion What are Sciety discussions?Listed in
This article is not in any list yet, why not save it to one of your lists.Abstract
Objective
To develop clinically operational, population-representative risk-score models for detecting metabolic syndrome (MetS) in U.S. adults by incorporating the NHANES survey design.
Research Design and Methods
We analyzed 36,812 U.S. adults from NHANES 1988– 2018. Seven models of increasing clinical complexity were trained and evaluated, ranging from basic demographics to full biochemical panels. We used a new deep-learning methodology for survey data with a predictive uncertainty quantification model.
Results
A model combining anthropometrics, vital signs and a basic lipid panel achieved an AUC of 0.923 at an estimated cost of 0.40€ per individual. Adding diabetes-specific biomarkers, including fasting plasma glucose (FPG) and glycated hemoglobin (HbA1c), yielded only marginal improvements.
Conclusions
This low-cost population-representative screening tool for MetS may help identify at-risk individuals and support data-driven public health interventions.
