Optimizing cancer classification: a hybrid RDO-XGBoost approach for feature selection and predictive insights.

ABSTRACT: The identification of relevant biomarkers from high-dimensional cancer data remains a significant challenge due to the complexity and heterogeneity inherent in various cancer types. Conventional feature selection methods often struggle to effectively navigate the vast solution space while maintaining high predictive accuracy. In response to these challenges, we introduce a novel feature selection approach that integrates Random Drift Optimization (RDO) with XGBoost, specifically designed to enhance the performance of cancer classification tasks. Our proposed framework not only improves classification accuracy but also offers valuable insights into the underlying biological mechanisms driving cancer progression. Through comprehensive experiments conducted on real-world cancer datasets, incl

SUBMITTER: Yaqoob A 

PROVIDER: S-EPMC11464649 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature