Predicting in-hospital all-cause mortality in heart failure using machine learning.

ABSTRACT:

Background

The age of onset and causes of heart failure differ between high-income and low-and-middle-income countries (LMIC). Heart failure patients in LMIC also experience a higher mortality rate. Innovative ways that can risk stratify heart failure patients in this region are needed. The aim of this study was to demonstrate the utility of machine learning in predicting all-cause mortality in heart failure patients hospitalised in a tertiary academic centre.

Methods

Six supervised machine learning algorithms were trained to predict in-hospital all-cause mortality using data from 500 consecutive heart failure patients with a left ventricular ejection fraction (LVEF) less than 50%.

Results

The mean age was 55.2 ± 16.8 years. There were 271 (54.2%) males, and the mean

SUBMITTER: Mpanya D 

PROVIDER: S-EPMC9875063 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature