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

Clinical Information and Decision Making (2029) Big Data (316) Methods and Instruments in Medical Informatics (208) Development and Evaluation of Research Methods, Instruments and Tools (785) Tools, Programs and Algorithms (399) Methodological Innovations (42) Personal Health Records, Patient-Accessible Electronic Health Records, Patient Portals (712) Machine Learning (2193) Recruitment of Research Participants (387)

Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/60231, first published .
Sample Size Requirements for Popular Classification Algorithms in Tabular Clinical Data: Empirical Study

Sample Size Requirements for Popular Classification Algorithms in Tabular Clinical Data: Empirical Study

Sample Size Requirements for Popular Classification Algorithms in Tabular Clinical Data: Empirical Study

Authors of this article:

Scott Silvey1 Author Orcid Image ;   Jinze Liu1 Author Orcid Image
Article Authors Cited by (20) Tweetations Metrics

Journals

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  2. Czyżewski B, Staniszewski J, Staniszewska J, Guth M. Does Increasing Agricultural Efficiency Contribute to Food Security—Trade‐Offs of Value Addition in Crop Production?. Sustainable Development 2025;33(S1):939 View
  3. Spittle A, Marschik P, Adde L, Badawi N, Byrne R, Bos A, Chatelin A, Coughlan J, Fedeli F, Guzzetta A, Ho E, Johnson M, Kwong A, McEwan A, Morgan C, Mughogho A, Murray D, Orlandi S, Peyton C, Prosser L, Ritterband-Rosenbaum A, Tran T, Zhang D, Passmore E. Towards universal early screening for cerebral palsy: a roadmap for automated General Movements Assessment. eClinicalMedicine 2025;86:103379 View
  4. Zaloumis S, Rajasekhar M, Simpson J. How to use learning curves to evaluate the sample size for malaria prediction models developed using machine learning algorithms. Malaria Journal 2025;24(1) View
  5. Vanhove A, Graham B, Titareva T, Udomvisawakul A. Classification Performance of Supervised Machine Learning to Predict Human Resource Management Outcomes: A Meta‐Analysis Using Cross‐Classified Multilevel Modeling. Human Resource Management 2025;64(6):1767 View
  6. Hu Y, Zhang X, Slavin V, Belsti Y, Tiruneh S, Callander E, Enticott J. Beyond Comparing Machine Learning and Logistic Regression in Clinical Prediction Modelling: Shifting from Model Debate to Data Quality. Journal of Medical Internet Research 2025;27:e77721 View
  7. Silvey S, Kamath P, George J, Choudhury A, Xie Q, Topazian M, Mekonnen H, Cao Z, Nagral A, Reddy K, Adebayo D, Asrani S, Rajoriya N, Arrese M, Aghayeva S, Sharma M, Huezo S, Gadano A, Yapici H, Debzi N, Shaw J, Albhaisi S, Pérez Hernández J, Wang Y, Peng F, Wei L, Eapen C, Tan H, Fung J, Rajaram R, Thanapirom K, Adanır H, Doyle A, Shalimar , Su M, Jothimani D, Cai Y, Velazquez R, Wang W, Gounder M, Gofton C, Barutcu S, Haktaniyan B, Farias A, Aravinthan A, Bera C, Singh S, Hayes P, Idilman R, Torre A, Alvares-da-Silva M, Seto W, Wong F, Bush B, Thacker L, Patel N, Bajaj J. Enhancement of Inpatient Mortality Prognostication With Machine Learning in a Prospective Global Cohort of Patients With Cirrhosis With External Validation. Gastroenterology 2026;170(1):148 View
  8. Habboub B, Oludowole E, Speer R, Masuch J, Berger U, Gosch M, Singler K. The prevalence of sarcopenia and sarcopenic obesity in a German geriatric day clinic. The Journal of Frailty & Aging 2025;14(5):100072 View
  9. Lis M, Banyś R, Solewski B, Stanek A, Krupiński M, Obuchowicz B, Puto T, Piórkowski A, Batko K. Chest X-Ray as a Screening Tool for Aortic Arch Dilation: CT-Based Evaluation of Reliability. Diagnostics 2025;15(20):2564 View
  10. Sharma S, Trivedi V, Utadiya S, Sheoran G, Anand A. Refractometer based on phase measuring deflectometry using smartphone and machine learning assisted analysis. Physica Scripta 2025;100(10):105540 View
  11. Das N, Ghosh P, Hossain M, Shuvo U, Talukder N, Khatun F, Chowdhury M, Ahammed B. Predicting hypertension and identifying most important factors among married women in Bangladesh using machine learning approach. PLOS One 2025;20(10):e0335442 View
  12. Alatau M, Bauer J, Sazonov V. Artificial Intelligence for Predicting Difficult Airways: A Review. Journal of Clinical Medicine 2025;14(23):8600 View
  13. Caporali A, Di Domenico A, D’Addario C, de Pasquale F. Machine learning for individual epigenetic fingerprints as predictors of well-being in young adults. Scientific Reports 2026;16(1) View
  14. Chechekhina E, Voloshin N, Solopov M, Tyurin-Kuzmin P, Kulebyakin K. Traditional machine learning in biomedical image analysis: before you go too deep. Frontiers in Artificial Intelligence 2026;9 View
  15. Becker S, Wayant N. Denoising of Binary Built-Up Maps Using Multi-Temporal Image Processing Thresholding. Land 2026;15(2):271 View
  16. Cantaş Türkiş F, Varol B, Golcuk Y, Karakoyun Ö. Toward an explainable AI-Based clinical decision support system for predicting adverse outcomes in Rhabdomyolysis. Informatics for Health and Social Care 2026:1 View
  17. Gagné D, Shajari E, Malick M, Roy P, Noël J, Gagnon H, Brunet M, Carrier J, Boisvert F, Beaulieu J. Exploring an Intermediate Colorectal Cancer Screening Test Based on Stool Proteomics and Machine Learning for Optimizing the Selection of Patients for Colonoscopy Identified from FIT. Molecular & Cellular Proteomics 2026:101534 View
  18. Zhao D. Critical considerations for the use of deep learning models in clinical oncology prediction. Frontiers in Oncology 2026;16 View
  19. Silvey S, Olex A, Tang S, Liu J. Sample size requirements for machine learning classification of binary outcomes in bulk RNA-Seq data. BMC Bioinformatics 2026;27(1) View

Books/Policy Documents

  1. Shchegoleva N, Tonka P, Zalutskaya N. Computational Science and Its Applications – ICCSA 2025 Workshops. View