Machine learning-based risk factor analysis and prediction model construction for mortality in chronic heart failure.

ABSTRACT:

Background

Given the high global mortality burden of chronic heart failure (CHF) and the limitations of traditional risk prediction tools in accuracy and comprehensiveness, along with the potential of machine learning (ML) to improve prediction performance and the ability of a health ecology framework to systematically identify multi-dimensional risk factors, we aimed to develop an ML-based mortality risk prediction model for CHF and analyse its risk factors using a health ecology framework.

Methods

We enrolled 489 CHF patients from the Jackson Heart Database, with all-cause mortality during a 10-year follow-up period designated as the outcome measure. Guided by a five-layer health ecology framework (individual traits, behavioural characteristics, interpersonal relationships

SUBMITTER: Xu Q 

PROVIDER: S-EPMC12427600 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature