Evaluation of machine learning methods for prediction of heart failure mortality and readmission: meta-analysis.

ABSTRACT:

Background

Heart failure (HF) impacts nearly 6 million individuals in the U.S., with a projected 46% increase by 2030, is creating significant healthcare burdens. Predictive models, particularly machine learning (ML)-based models, offer promising solutions to identify patients at greater risk of adverse outcomes, such as mortality and hospital readmission. This review aims to assess the effectiveness of ML models in predicting HF-related outcomes, with a focus on their potential to improve patient care and clinical decision-making. We aim to assess how effectively machine learning models predict mortality and readmission in heart failure patients to improve clinical outcomes.

Method

The study followed PRISMA 2020 guidelines and was registered in the PROSPERO database (CRD420

SUBMITTER: Hajishah H 

PROVIDER: S-EPMC11974104 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature