Comparing Machine Learning Models and Statistical Models for Predicting Heart Failure Events: A Systematic Review and Meta-Analysis.

ABSTRACT:

Objective

To compare the performance, clinical feasibility, and reliability of statistical and machine learning (ML) models in predicting heart failure (HF) events.

Background

Although ML models have been proposed to revolutionize medicine, their promise in predicting HF events has not been investigated in detail.

Methods

A systematic search was performed on Medline, Web of Science, and IEEE Xplore for studies published between January 1, 2011 to July 14, 2021 that developed or validated at least one statistical or ML model that could predict all-cause mortality or all-cause readmission of HF patients. Prediction Model Risk of Bias Assessment Tool was used to assess the risk of bias, and random effect model was used to evaluate the pooled c-statistics of included mode

SUBMITTER: Sun Z 

PROVIDER: S-EPMC9020815 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature