Machine learning-based model for worsening heart failure risk in Chinese chronic heart failure patients.

ABSTRACT:

Aims

This study aims to develop and validate an optimal model for predicting worsening heart failure (WHF). Multiple machine learning (ML) algorithms were compared, and the results were interpreted using SHapley Additive exPlanations (SHAP). A clinical risk calculation tool was subsequently developed based on these findings.

Methods and results

This nested case-control study included 200 patients with chronic heart failure (CHF) from the China-Japan Friendship Hospital (September 2019 to December 2022). Sixty-five variables were collected, including basic information, physical and chemical examinations, and quality of life assessments. WHF occurrence within a 3-month follow-up was the outcome event. Variables were screened using LASSO regression, univariate analysis, and com

SUBMITTER: Sun Z 

PROVIDER: S-EPMC11769658 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature