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

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 maintainin...

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Bibliographic Details
Published in:	Cancer Immunology, Immunotherapy : CII Vol. 73; no. 12; p. 261
Main Authors:	Yaqoob, Abrar, Verma, Navneet Kumar, Aziz, Rabia Musheer, Shah, Mohd Asif
Format:	Journal Article
Language:	English
Published:	Berlin/Heidelberg Springer Berlin Heidelberg 09.10.2024 Springer Nature B.V
Subjects:	Accuracy Algorithms Bayes Theorem Biomarkers, Tumor - genetics Breast cancer Cancer Cancer Research Central nervous system Classification Computational Biology - methods Feature selection Female Humans Immunology Leukemia Medicine Medicine & Public Health Neoplasms - classification Oncology Ovarian cancer Ovaries Support Vector Machine Microarray data analysis Feature selection Random drift optimization Cancer classification XGBoost
ISSN:	1432-0851, 0340-7004, 1432-0851
Online Access:	Get full text
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