A Deep Learning-Based Predictive Algorithm for Metabolic Syndrome Detection in the U.S. Population
Correa Segade, C.; Solozabal, R.; Hammouri, Z. A. A.; Gomez-Peralta, F.; Rossman, H.; Vidal, J. C.; Klonoff, D. C.; Segal, E.; Matabuena, M.
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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 eur 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.
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