A machine learning tool for identifying newly diagnosed heart failure in individuals with known diabetes in primary care.

ABSTRACT:

Aims

We aimed to create a predictive model utilizing machine learning (ML) to identify new cases of congestive heart failure (CHF) in individuals with diabetes in primary health care (PHC) through the analysis of diagnostic data.

Methods

We used a sex- and age-matched case-control design. Cases of new CHF were identified across all outpatient care settings 2015-2022 (n = 9098). We included individuals 30 years and above, by sex and age groups of 30-65 years and >65 years. The controls (five per case) were sampled from the individuals in 2015-2022 without CHF at any time between 2010 and 2022, in total 45 490. From the stochastic gradient boosting (SGB) technique model, we obtained a rank of the 10 most important factors related to newly diagnosed CHF in individuals with diab

SUBMITTER: Wandell P 

PROVIDER: S-EPMC11769636 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature