Development of an interpretable machine learning model for early prediction of aortic stiffness risk in health examination populations.

ABSTRACT:

Background

Carotid-femoral pulse wave velocity (cfPWV) is the gold standard for assessing aortic stiffness, but its complexity, time consumption, and privacy concerns limit its application in routine health examinations. This study aimed to develop an interpretable machine learning model based on readily accessible indicators, providing an alternative screening tool for institutions unable to perform cfPWV measurements.

Methods

A total of 261 participants were enrolled as the development cohort and randomly divided into training and testing sets (7:3 ratio), with 101 additional participants as an independent external validation set. Aortic stiffness was defined as cfPWV ≥10 m/s. Features were selected by combining univariate logistic regression, recursive feature elimination

SUBMITTER: Zhou Z 

PROVIDER: S-EPMC12819839 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature