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

Stroke (148) Clinical Information and Decision Making (1320) Machine Learning (1378)

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
Article Authors Cited by (3) Tweetations (2) Metrics

Journals

  1. Abujaber A, Albalkhi I, Imam Y, Nashwan A, Yaseen S, Akhtar N, Alkhawaldeh I. Predicting 90-Day Prognosis in Ischemic Stroke Patients Post Thrombolysis Using Machine Learning. Journal of Personalized Medicine 2023;13(11):1555 View
  2. Bui H, Nguyễn Thị Phương Q, Cam Tu H, Nguyen Phuong S, Pham T, Vu T, Nguyen Thi Thu H, Khanh Ho L, Nguyen Tien D. The Roles of NOTCH3 p.R544C and Thrombophilia Genes in Vietnamese Patients With Ischemic Stroke: Study Involving a Hierarchical Cluster Analysis. JMIR Bioinformatics and Biotechnology 2024;5:e56884 View