Development and external validation of a machine learning model for cardiac valve calcification early screening in dialysis patients: a multicenter study.

ABSTRACT:

Background

Cardiac valve calcification (CVC) is common in dialysis patients and associated with increased cardiovascular risk. However, early screening has been limited by cost concerns. This study aimed to develop and validate a machine learning model to enhance early detection of CVC.

Methods

Data were collected at four centers between 2020 and 2023, including 852 dialysis patients in the development dataset and 661 in the external validation dataset. Predictive factors were selected using LASSO regression combined with univariate and multivariate analyses. Machine learning models including CatBoost, XGBoost, decision tree, support vector machine, random forest, and logistic regression were used to develop the CVC risk model. Model performance was evaluated in both validat

SUBMITTER: Wang X 

PROVIDER: S-EPMC12035951 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature