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July 23, 2026BMC Cardiovascular DisordersOpen Access

Advanced prediction of cardiovascular-kidney-metabolic syndrome using eight machine learning models and 24 composite indices

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Authors

MLMinghao LiJXJunyuan XiangFXFang Xiao

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Overview

Randomized trial demonstrates effective risk stratification for cardiovascular-kidney-metabolic syndrome using machine learning in diverse populations, indicating clinical potential.

Key Points

  • This research aims to develop and validate a machine learning classifier for predicting stages of cardiovascular-kidney-metabolic syndrome using standard indices.
  • Analyzed data from 12,106 participants in NHANES survey.
  • Selected 10 biomarkers through feature selection after addressing multicollinearity.
  • Evaluated several machine learning algorithms, with LightGBM showing the highest performance (ROC AUC: 0.88).
  • LightGBM model achieved a ROC AUC of 0.88 for predicting CKM syndrome stages.
  • External validation with CHARLS dataset yielded a ROC AUC of 0.84.
  • SHAP analysis identified metabolic markers such as eGDR and TyG as key predictors for CKM stages.

Cite This Study

Li et al. (2026) studied this question.

synapsesocial.com/papers/6a61aeeefaa9903c51169e82https://doi.org/10.1186/s12872-026-06318-2
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