The effect of feature normalization methods in radiomics.

ABSTRACT:

Objectives

In radiomics, different feature normalization methods, such as z-Score or Min-Max, are currently utilized, but their specific impact on the model is unclear. We aimed to measure their effect on the predictive performance and the feature selection.

Methods

We employed fifteen publicly available radiomics datasets to compare seven normalization methods. Using four feature selection and classifier methods, we used cross-validation to measure the area under the curve (AUC) of the resulting models, the agreement of selected features, and the model calibration. In addition, we assessed whether normalization before cross-validation introduces bias.

Results

On average, the difference between the normalization methods was relatively small, with a gain of at most + 0

SUBMITTER: Demircioglu A 

PROVIDER: S-EPMC10772134 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature