Machine learning to predict abnormal myocardial perfusion from pre-test features.

ABSTRACT:

Background

Accurately predicting which patients will have abnormal perfusion on MPI based on pre-test clinical information may help physicians make test selection decisions. We developed and validated a machine learning (ML) model for predicting abnormal perfusion using pre-test features.

Methods

We included consecutive patients who underwent SPECT MPI, with 20,418 patients from a multi-center (5 sites) international registry in the training population and 9019 patients (from 2 separate sites) in the external testing population. The ML (extreme gradient boosting) model utilized 30 pre-test features to predict the presence of abnormal myocardial perfusion by expert visual interpretation.

Results

In external testing, the ML model had higher prediction performance for ab

SUBMITTER: Miller RJH 

PROVIDER: S-EPMC9588501 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature