Application of an interpretable machine learning model based on optimal feature selection for predicting triple-vessel coronary disease: a multicenter retrospective study.

ABSTRACT:

Objective

This study aimed to evaluate the predictive value of the remnant cholesterol-inflammatory index (RCII) in assessing the risk of triple-vessel disease (TVD), and to construct a comparative framework of predictive models using six machine learning algorithms based on RCII and other clinical features for identifying high-risk individuals.

Methods

In this retrospective multicenter study, we enrolled 2,911 patients who underwent coronary angiography between January 1, 2024, and December 31, 2024, at two tertiary hospitals. Clinical and laboratory data were collected. Feature selection was performed using both Least Absolute Shrinkage and Selection Operator (LASSO) regression and multivariate logistic regression. Six machine learning (ML) algorithms were trained for risk

SUBMITTER: Hou L 

PROVIDER: S-EPMC12700116 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature