Performance Analysis of Data Augmentation Techniques for Lung Cancer Classification using Microarray Data

Cancer gene expression data analysis using microarray technology is a critical domain in understanding the molecular intricacies of malignancy. With an emphasis on the incorporation of data augmentation techniques, this paper offers a thorough investigation into the categorization of binary class Lung cancer gene expression dataset. Effective dimensionality reduction is achieved by using univariate feature selection technique called SelectKBest method. Through the evaluation of performance indicators including accuracy and recall, the study offers detailed insights into the advantages and disadvantages of each augmentation strategy. The study’s findings add to the current conversation in precision medicine and provide information for improving methods for analyzing gene expression data in relation to cancer classification.