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

Machine Learning (2173) Artificial Intelligence (2252) Dental Sciences (179) Decision Support for Health Professionals (1594) Pediatric Dentistry and Orthodontics (20) Computer-Aided Diagnosis (268) AI Applications in Public Health (33)

Published on in Vol 13 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/75117, first published .
A Machine Learning Algorithm With an Oversampling Technique in Limited Data Scenarios for the Prediction of Present and Future Restorative Treatment Need: Development and Validation Study

A Machine Learning Algorithm With an Oversampling Technique in Limited Data Scenarios for the Prediction of Present and Future Restorative Treatment Need: Development and Validation Study

A Machine Learning Algorithm With an Oversampling Technique in Limited Data Scenarios for the Prediction of Present and Future Restorative Treatment Need: Development and Validation Study

Authors of this article:

Elina Väyrynen1 Author Orcid Image ;   Otso Tirkkonen1 Author Orcid Image ;   Henna Tiensuu2 Author Orcid Image ;   Jaakko Suutala2 Author Orcid Image ;   Vuokko Anttonen1 Author Orcid Image ;   Marja-Liisa Laitala1, 3 Author Orcid Image ;   Katri Kukkola4 Author Orcid Image ;   Saujanya Karki1 Author Orcid Image
Article Authors Cited by (2) Tweetations Metrics

Journals

  1. Tirkkonen O, Tiensuu H, Väyrynen E, Suutala J, Vuollo V, Laitala M, Karki S. An explainable and transparent machine learning approach for predicting dental caries: a cross-national validation study. BMC Oral Health 2026;26(1) View
  2. Wang L, Shi E, Meyers B, Vlachos P, Tcheng J, Denardo S. Machine learning performance for a small dataset: random oversampling improves data imbalances and fairness. BMC Medical Research Methodology 2026 View