Establishment and validation of a machine learning model to stratify malnutrition risk in hospitalized older patients with chronic heart failure.

ABSTRACT:

Background and objectives

Malnutrition among older hospitalized adults with chronic heart failure (CHF) is associated with adverse clinical outcomes, yet reliable early risk stratification tools remain lacking. This study aimed to develop and validate a machine learning (ML) model for malnutrition risk stratification in this population.

Methods and study design

Malnutrition among older hospitalized adults with chronic heart failure (CHF) is associated with adverse clinical outcomes, yet reliable early risk stratification tools remain lacking. This study aimed to develop and validate a machine learning (ML) model for malnutrition risk stratification in this population.

Results

Malnutrition prevalence was 44.1% (348/790). In the internal testing, CatBoost (CAT) achieved

SUBMITTER: Sun Q 

PROVIDER: S-EPMC12823253 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature