Analysis of Different Variants of Relief Based Algorithms for Feature Selection in Medical Applications

This research paper explores the efficacy of Relief-based algorithms (RBAs) in the domain of medical data analysis, with a specific focus on lung cancer classification. Faced with the growing complexity of biological data and the need for computationally efficient yet sophisticated feature selection methods, RBAs emerge as a compelling solution. Various RBA variants, including ReliefF, Iterative Relief, I-Relief, SURF and MultiSURF, offer distinct perspectives on feature weighting and selection. Empirical analysis on lung cancer datasets reveals notable results, highlighting the impressive 0.80 accuracy rate achieved by I-Relief in classification tasks, thus confirming its effectiveness. These findings contribute to a deeper understanding of RBA capabilities and provide valuable insights for their future application and refinement within the domain of medical data analysis.