Radiomics machine learning study with a small sample size: Single random training-test set split may lead to unreliable results

This study aims to determine how randomly splitting a dataset into training and test sets affects the estimated performance of a machine learning model and its gap from the test performance under different conditions, using real-world brain tumor radiomics data. We conducted two classification tasks...

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Detalles Bibliográficos
Autores principales: An, Chansik, Park, Yae Won, Ahn, Sung Soo, Han, Kyunghwa, Kim, Hwiyoung, Lee, Seung-Koo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8360533/
https://www.ncbi.nlm.nih.gov/pubmed/34383858
http://dx.doi.org/10.1371/journal.pone.0256152

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8360533/
https://www.ncbi.nlm.nih.gov/pubmed/34383858
http://dx.doi.org/10.1371/journal.pone.0256152

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