Applications of Deep Learning in Predicting the Risk of Metabolic Syndrome from Lifestyle and Behavioral Factors: A Scoping Review
Oluwatope R. Ojo *
Department of Mathematics & Statistics, East Tennessee State University, Johnson City, Tennessee, United States.
Victor T. Aderanti
Department of Mathematics & Statistics, East Tennessee State University, Johnson City, Tennessee, United States.
Joseph Chimezie
Epigenetics and Molecular Biology Laboratory, Federal University of Technology, Akure, Nigeria and Department of Physiology, School of Basic Medical Sciences, Federal University of Technology, Akure, Nigeria.
Worship O. Agbonifo
Epigenetics and Molecular Biology Laboratory, Federal University of Technology, Akure, Nigeria and Department of Physiology, School of Basic Medical Sciences, Federal University of Technology, Akure, Nigeria.
Mercy O. Awoleye
Epigenetics and Molecular Biology Laboratory, Federal University of Technology, Akure, Nigeria and Department of Physiology, School of Basic Medical Sciences, Federal University of Technology, Akure, Nigeria.
Hope O. Francis
Epigenetics and Molecular Biology Laboratory, Federal University of Technology, Akure, Nigeria and Department of Physiology, School of Basic Medical Sciences, Federal University of Technology, Akure, Nigeria.
Temitope G. Adedeji
Epigenetics and Molecular Biology Laboratory, Federal University of Technology, Akure, Nigeria and Department of Physiology, School of Basic Medical Sciences, Federal University of Technology, Akure, Nigeria.
*Author to whom correspondence should be addressed.
Abstract
Background: Metabolic syndrome (MetS) affects roughly one quarter of the world’s adults and dramatically heightens cardiometabolic morbidity, mortality, and healthcare costs. Because the syndrome is heavily driven by modifiable lifestyle and behavioural factors, risk stratification that relies only on easily collected, non-invasive information would enable earlier, lower-cost intervention. Deep learning (DL) models are theoretically well-suited to capture the complex, non-linear interactions among these heterogeneous data types, yet the relevant evidence base remains diffuse.
Objective: To map and critically appraise the ways in which DL has been applied to predict MetS risk from lifestyle and behavioural variables, identify the predictors and model designs that dominate current practice, and highlight gaps and opportunities for future work.
Methodology: A PRISMA-ScR–guided search (PubMed, Google Scholar; 2010 – 2025) located empirical studies based on predefined inclusion and exclusion criteria. Seven (7) studies met all criteria. Data on setting, sample, predictor categories, DL design, validation approach, and performance metrics were charted and synthesised narratively.
Results: Of the seven (7) studies included, most used feed-forward neural networks on cross-sectional cohorts (n = 468–70 370) in Iran, Mexico, Taiwan, and South Korea. Accuracies ranged from ~0.81 to 0.94; AUCs often surpassed 0.85 and peaked at 0.99 when polygenic risk scores were added. Waist circumference, BMI, and blood-pressure indices were the strongest predictors, while activity, diet, sleep, smoking, and alcohol intake improved performance.
Conclusion: Despite small numbers and limited geographic reach, evidence suggests that DL can turn routine, non-invasive data into highly accurate MetS risk tools.
Keywords: Metabolic syndrome, deep learning, lifestyle factors, behavioural risk factors, non-invasive prediction, machine learning