Effect of machine learning re-sampling techniques for imbalanced datasets in 18F-FDG PET-based radiomics model on prognostication performance in cohorts of head and neck cancer patients.

Purpose: Biomedical data frequently contain imbalance characteristics which make achieving good predictive performance with data-driven machine learning approaches a challenging task. In this study, we investigated the impact of re-sampling techniques for imbalanced datasets in PET radiomics-based p...

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Publicado en:	European Journal of Nuclear Medicine & Molecular Imaging Vol. 47; no. 12; pp. 2826 - 2836
Autores principales:	Xie, Chenyi, Du, Richard, Ho, Joshua WK, Pang, Herbert H, Chiu, Keith WH, Lee, Elaine YP, Vardhanabhuti, Varut
Formato:	Journal Article
Publicado:	Springer Nature Nov2020
Acceso en línea:	Ver este registro en EBSCOhost