Impact of train/test sample regimen on performance estimate stability of machine learning in cardiovascular imaging.

ABSTRACT: As machine learning research in the field of cardiovascular imaging continues to grow, obtaining reliable model performance estimates is critical to develop reliable baselines and compare different algorithms. While the machine learning community has generally accepted methods such as k-fold stratified cross-validation (CV) to be more rigorous than single split validation, the standard research practice in medical fields is the use of single split validation techniques. This is especially concerning given the relatively small sample sizes of datasets used for cardiovascular imaging. We aim to examine how train-test split variation impacts the stability of machine learning (ML) model performance estimates in several validation techniques on two real-world cardiovascular imaging datasets: st

SUBMITTER: Singh V 

PROVIDER: S-EPMC8280147 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature