This paper is in the following e-collection/theme issue:

Stroke (166) Clinical Information and Decision Making (1434) Machine Learning (1501)

Published on in Vol 7 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/47736, first published .
Comparing Explainable Machine Learning Approaches With Traditional Statistical Methods for Evaluating Stroke Risk Models: Retrospective Cohort Study

Comparing Explainable Machine Learning Approaches With Traditional Statistical Methods for Evaluating Stroke Risk Models: Retrospective Cohort Study

Comparing Explainable Machine Learning Approaches With Traditional Statistical Methods for Evaluating Stroke Risk Models: Retrospective Cohort Study

Authors of this article:

Sermkiat Lolak1 Author Orcid Image ;   John Attia2 Author Orcid Image ;   Gareth J McKay3 Author Orcid Image ;   Ammarin Thakkinstian1 Author Orcid Image
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