Features selection
REGRESSION
Due to the high complexity of the extracted features, there was a risk of overfitting in the analysis. Hence, the dimensions presented by these quantitative features needed to be reduced by prioritizing the features. The least absolute shrinkage and selection operator (LASSO) is a popular high‐dimensional data analysis method that can be used to improve both prediction accuracy and interpretation. This approach can estimate the regression coefficients for every feature and successively shrink them to avoid inflation of the estimated coefficients, resulting in superior predictive performance and irrelevant features. LASSO was conducted to choose the optimized subset of features to construct the final model.LASSO was first used to tune the regularization parameter λ using 10‐fold cross‐validation, and the optimized Texture feature selection using the least absolute shrinkage and the histogram of the Rad‐score based on the selected features. The optimal λ value of 0.026 was selected.