Development of a flexible feature selection framework in radiomics-based prediction modeling: Assessment with four real-world datasets.

ABSTRACT: There are several important challenges in radiomics research; one of them is feature selection. Since many quantitative features are non-informative, feature selection becomes essential. Feature selection methods have been mixed with filter, wrapper, and embedded methods without a rule of thumb. This study aims to develop a framework for optimal feature selection in radiomics research. We developed the framework that the optimal features were selected to quickly through controlling relevance and redundancy among features. A 'FeatureMap' was generated containing information for each step and used as a platform. Through this framework, we can explore the optimal combination of radiomics features and evaluate the predictive performance using only selected features. We assessed the framework u

SUBMITTER: Hong S 

PROVIDER: S-EPMC11599926 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature