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

Machine Learning (3373) Clinical Informatics (2424) Artificial Intelligence in Diabetes Care and Prevention (117) Chronic Foot Ulcer and Wounds (40) Applications of AI (1126)

Published on in Vol 13 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/71994, first published .
Close-up of a human foot with a red, inflamed toe and a digital network overlay.

Evaluation of Machine Learning Model Performance in Diabetic Foot Ulcer: Retrospective Cohort Study

Evaluation of Machine Learning Model Performance in Diabetic Foot Ulcer: Retrospective Cohort Study

Authors of this article:

Veerle Y van Velze1, 2 Author Orcid Image ;   Hendrico L Burger3 Author Orcid Image ;   Tim J van der Steenhoven1, 4 Author Orcid Image ;   Hani Al-Ers3 Author Orcid Image ;   Lauren N Goncalves1 Author Orcid Image ;   Daniël Eefting1, 4, 5 Author Orcid Image ;   Willem-Jan J de Jong1, 4 Author Orcid Image ;   Harm J Smeets1, 4 Author Orcid Image ;   Janna C Specken Welleweerd1, 4 Author Orcid Image ;   Joost R van der Vorst4, 5 Author Orcid Image ;   Sandy Uchtmann1 Author Orcid Image ;   Robert Rissmann2, 6, 7 Author Orcid Image ;   Jaap F Hamming4, 5 Author Orcid Image ;   Lampros Stergioulas3 Author Orcid Image ;   Koen EA van der Bogt1, 4, 5 Author Orcid Image
Article Authors Cited by (4) Tweetations Metrics

Journals

  1. Zhang H, Fan W, Li P, Chen X, Yin S. Predicting the diabetic foot in patients with type 2 diabetes mellitus based on machine learning. BioMedical Engineering OnLine 2026;25(1) View
  2. Kęczkowska J, Płaza M, Siermontowski P, Henrykowska G. Artificial Intelligence in Hyperbaric Medicine: Examples of Applications. Polish Hyperbaric Research 2026;94(1):57 View
  3. Wang X, Li C, Tuo Y, Zhao B, Xiahati A, Fan L, Wahafu P. Development and temporal validation of an explainable machine learning model for predicting in-hospital diabetic foot ulcer in patients with type 2 diabetes using inflammatory–nutritional composite indices. Frontiers in Medicine 2026;13 View
  4. Klooster I, Machens A, Kip H, Kelders S, van Gemert-Pijnen L. Using electronic health record data to develop a multivariable prediction model for diabetic foot ulcers in patients with diabetes: A case study exploring opportunities for just-in-time adaptive interventions. DIGITAL HEALTH 2026;12 View