Humans17,981 laboratory recordsCross-sectional study

Screening Signals of Reference-Defined Metabolic Syndrome Using HbA1c and LDL Cholesterol: An Explainable Machine Learning Study

Figure 1. Flow chart of study population selection, analytical dataset preparation, and patient-level data partitioning.
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Figure 1. Flow chart of study population selection, analytical dataset preparation, and patient-level data partitioning.Flow chart of study population selection, analytical dataset preparation, and patient-level data partitioning.Demir · CC BY

Diagnostics

Abstract

Background: Metabolic syndrome (MetS) is characterized by the clustering of central adiposity, elevated blood pressure, dysglycemia, and atherogenic dyslipidemia. Machine learning models for metabolic syndrome may show inflated performance when predictors overlap with the diagnostic criteria used to define the reference outcome.

The paper

Tokat Gaziosmanpaşa Üniversitesi

Diagnostics · 20 Sep 2026 · CC BY

doi.org/10.3390/diagnostics16183048PubMed 42793833

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