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Microarray technology enables biologists to simultaneously monitor the activities of genome-wide features. This method generates gene expression data that can be used to classify cancers. Currently, disease diagnosis is based on gene expression data. However, gene expression data are regarded as high-dimensional data because they contain noisy, irrelevant, and redundant genes that are not required for categorization. Though, the curses of dimensionality and sparsity complicate the challenge of categorizing gene expression profiles. The curse of dimensionality is a significant obstacle to overcome. This is a computationally difficult task due to the vast number of genes with relatively few samples in gene expression data. To overcome these challenges, we presented an efficient dimensionality reduction for microarray gene expression. This paper aims to propose machine learning–based strategies for classifying acute myeloid leukemia and acute lymphoblastic leukemia based on microarray gene expression profiles. We employed logistic regression, extremely randomized trees classifier, ridge classifier, Ada boost classifier, linear discriminant analysis, random forest, gradient boosting, a

Author

Yakub Kayode Saheed

Publisher

Elsevier Science & Technology

Published

2023

Language

EN

ISBN

9780323983525

Subjects

Technology, Science, Medical

Updated

2026-03-24