Machine Learning in Metabolic Syndrome: Toward Precision Diagnosis, Risk Stratification and Personalised Intervention

Metabolic syndrome (MetS) — a cluster of interrelated cardiometabolic risk factors including central obesity, insulin resistance, dyslipidemia, and hypertension — affects approximately 25–35% of adults worldwide and confers a 2–3-fold increased risk of cardiovascular disease and type 2 diabetes mellitus. The heterogeneous, multifactorial nature of MetS renders traditional diagnostic and therapeutic frameworks insufficient for capturing its biological complexity. Machine learning (ML) has emerged as a transformative analytical paradigm, offering unprecedented capacity to model non-linear interactions among genomic, metabolomic, clinical, and lifestyle variables. This review synthesises current evidence on ML applications in MetS, spanning early detection and risk prediction to omics integration and digital health optimisation. We critically evaluate algorithmic approaches — including ensemble methods, deep learning, and natural language processing — and discuss key challenges around data quality, model interpretability, algorithmic bias, and clinical translation.