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
Article Authors Cited by (4) Tweetations (2) Metrics

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  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
  3. Inchingolo F, Inchingolo A, Piras F, Ferrante L, Mancini A, Palermo A, Inchingolo A, Dipalma G. Management of Patients Receiving Anticoagulation Therapy in Dental Practice: A Systematic Review. Healthcare 2024;12(15):1537 View