SVM-Based Classification of Microarrays Gene Expression Data

Abstract

Classifying microarray gene expression data is crucial due to its high-dimensional na ture and its significant impact on disease diagnosis and personalized treatment strate gies. Timely and accurate classification of gene expression data greatly influences treat ment outcomes and patient survival rates. Traditionally, gene expression data analysis involves various statistical methods. However, with the emergence of advanced ma chine learning techniques, automated classification within these datasets becomes cru cial. Present methodology typically involve SVM classifier with different kernel functions to classify diverse gene expression profiles. Nonetheless, the varied characteristics within gene expression data present notable classification challenges. In our study, we introduce a comprehensive dataset comprising thousands of gene ex pression profiles from Leukemia cancer. Our approach involves proposing an optimal classification method by fine-tuning Support Vector Machine (SVM) parameters and selecting the most appropriate kernel functions. We utilize both standard and refined SVMs with various kernel functions, including linear, polynomial, radial basis func tion (RBF), and sigmoid, alongside penalized SVM models using L1, Smoothly Clipped Absolute Deviation (SCAD), and SCAD + L2 penalties to improve classification per formance. Notably, our innovative approach, when applied to refined SVM with linear and poly nomial kernels, achieves superior performance, with the L1 norm exhibiting the best classification accuracy among penalized models. This breakthrough marks a significant advancement in gene expression data classification literature, highlighting the potential of SVMs, particularly with linear and polynomial kernels combined with appropriate penalty terms, for precise and efficient disease classification.