Sparsity regularization enhances gene selection and leukemia subtype classification via logistic regression

Abstract

This study investigated the application of sparsity regularization methods to improve the classification of leukemia subtypes using high-dimensional gene expression data. Multinomial logistic regression models with the sparsity methods of Ridge, Lasso, and Elastic Net regularizations were employed to address overfitting and dimensionality issues while enhancing model interpretability. The study used a leukemia cancer dataset from the Curated Microarray Database (CuMiDa), which included gene expression data for 16,383 genes across 281 samples representing seven different types of leukemia. The statistical metrics of Accuracy, Kappa statistics, AUC, and F1-score were used to measure the models' implementation. Besides, the effectiveness and ability of each method in gene selection and dimensional reduction of the models were discussed. Elastic Net regularization was a better technique than the Ridge and Lasso based on overall classification performance; it also reached the highest accuracy along with Kappa values. On the other hand, both Lasso and Elastic Net were making more effective feature selections, creating sparse models that could efficiently discriminate leukemia subtypes. In this way, the results highlighted that the inclusion of sparsity regularization could enhance knowledge and accuracy in such a challenging task of subclass leukemia classification, enabling much more tailored treatments.