Comparative performance of multiple ensemble learning models for preoperative prediction of tumor de...
Comparative performance of multiple ensemble learning models for preoperative prediction of tumor deposits in rectal cancer based on MR imaging
About this item
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Author / Creator
Wang, Jiayi , Hu, Fayong , Li, Jin , Lv, Wenzhi , Liu, Zhiyong and Wang, Liang
Publisher
London: Nature Publishing Group UK
Journal title
Language
English
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Publication information
Publisher
London: Nature Publishing Group UK
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Scope and Contents
Contents
Ensemble learning can effectively mitigate the risk of model overfitting during training. This study aims to evaluate the performance of ensemble learning models in predicting tumor deposits in rectal cancer (RC) and identify the optimal model for preoperative clinical decision-making. A total of 199 RC patients were analyzed, with radiomic feature...
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Full title
Comparative performance of multiple ensemble learning models for preoperative prediction of tumor deposits in rectal cancer based on MR imaging
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Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_bb4c091ea42e41d4904a7f1edcff9f25
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_bb4c091ea42e41d4904a7f1edcff9f25
Other Identifiers
ISSN
2045-2322
E-ISSN
2045-2322
DOI
10.1038/s41598-025-89482-3
