Comparative performances of machine learning algorithms in radiomics and impacting factors.

ABSTRACT: There are no current recommendations on which machine learning (ML) algorithms should be used in radiomics. The objective was to compare performances of ML algorithms in radiomics when applied to different clinical questions to determine whether some strategies could give the best and most stable performances regardless of datasets. This study compares the performances of nine feature selection algorithms combined with fourteen binary classification algorithms on ten datasets. These datasets included radiomics features and clinical diagnosis for binary clinical classifications including COVID-19 pneumonia or sarcopenia on CT, head and neck, orbital or uterine lesions on MRI. For each dataset, a train-test split was created. Each of the 126 (9 × 14) combinations of feature selection algorit

SUBMITTER: Decoux A 

PROVIDER: S-EPMC10462640 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature