Enhancing Cancer Classification Through Ensemble Machine Learning and Gene Selection Approaches

Abstract

The vast dimensionality of gene expression data and the limited number of relevant genes necessitate the adoption of gene selection techniques. Furthermore, the choice of an efficient classifier plays a pivotal role in achieving accurate results. In this study, we employ the Minimum Redundancy Maximum Relevance (mRMR) method for gene selection, coupled with ensemble classifiers and individual classifiers like K-Nearest Neighbors (KNN) and Decision Trees (DT). A comparative analysis between two ensemble classifiers and two individual classifiers is conducted, revealing the superior performance of the ensemble classifiers. Our investigation utilizes four distinct cancer gene expression datasets to showcase the efficacy of employing ensemble classifiers and gene selection methods for cancer classification. The ensemble classifier (Bagging Classifier), in conjunction with the MRMR method selecting only the top 30 genes, achieved an impressive overall accuracy of 94% across all four employed datasets.