Classification of pre-metabolic syndrome and metabolic syndrome using machine learning and routine clinical variables in Thai adults

Background

Metabolic syndrome (MetS) and pre-metabolic syndrome (pre-MetS) are increasingly recognized as important stages of metabolic dysfunction in Asian populations. Conventional diagnostic approaches rely on fixed threshold criteria, whereas metabolic dysfunction occurs along a continuous spectrum. Machine learning (ML) can integrate multidimensional clinical data and model nonlinear relationships among clinical variables, potentially providing complementary information for distinguishing metabolic states. This study evaluated ten ML algorithms for distinguishing pre-MetS from MetS in Thai adults and identified features contributing most strongly to model-based classification.