A Heuristic Radiomics Feature Selection Method Based on Frequency Iteration and Multi-Supervised Training Mode

Radiomics is a non-invasive method for extracting quantitative and higher-dimensional features from medical images for diagnosis. It has received great attention due to its huge application prospects in recent years. We can know that the number of features selected by the existing radiomics feature selection methods is basically about ten. In this paper, a heuristic feature selection method based on frequency iteration and multiple supervised training mode is proposed. Based on the combination between features, it decomposes all features layer by layer to select the optimal features for each layer, then fuses the optimal features to form a local optimal group layer by layer and iterates to the global optimal combination finally. Compared with the current method with the best prediction performance in the three data sets, this method proposed in this paper can reduce the number of features from about ten to about three without losing classification accuracy and even significantly improving classification accuracy. The proposed method has better interpretability and generalization ability, which gives it great potential in the feature selection of radiomics.