Predicting the risk of mortality and rehospitalization in heart failure patients: A retrospective cohort study by machine learning approach.

ABSTRACT:

Background

Heart failure (HF) is a global problem, affecting more than 26 million people worldwide. This study evaluated the performance of 10 machine learning (ML) algorithms and chose the best algorithm to predict mortality and readmission of HF patients by using The Fasa Registry on Systolic HF (FaRSH) database.

Hypothesis

ML algorithms may better identify patients at increased risk of HF readmission or death with demographic and clinical data.

Methods

Through comprehensive evaluation, the best-performing model was used for prediction. Finally, all the trained models were applied to the test data, which included 20% of the total data. For the final evaluation and comparison of the models, five metrics were used: accuracy, F1-score, sensitivity, specificity and Area

SUBMITTER: Ketabi M 

PROVIDER: S-EPMC10894620 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature