ArticleCancer immunology, immunotherapy : CII2024
Optimizing cancer classification: a hybrid RDO-XGBoost approach for feature selection and predictive insights.
Article in Cancer immunology, immunotherapy : CII, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Performance comparison and future perspectives of deep learning and classical machine learning in bone tumor applications: a systematic review (2019-2025).BMC medical informatics and decision making · 2026Pooled it
- Machine learning algorithms in the estimation of sex from 3DCT-generated cranial and pelvic measurements.International journal of legal medicine · 2026Article
- A machine learning‑enhanced serum metabolomics model for non‑invasive detection of gastric cancer.Metabolomics : Official journal of the Metabolomic Society · 2026Article
- Classification of different light colors applied during the incubation period based on small intestine morphology with XGBoost algorithm.BMC veterinary research · 2026Article
- Robust feature selection for cancer microarray data using a hybrid mRMR and Binary Lion Optimization Algorithm.Scientific reports · 2026Article
- Hybrid deep learning framework for accurate classification of high dimensional genomic data.Scientific reports · 2026Article
- Artificial intelligence and machine learning-driven advancements in gastrointestinal cancer: Paving the way for precision medicine.World journal of gastroenterology · 2026Review
- Discriminative biomarker selection using hybrid multi-population evolutionary computation.Scientific reports · 2025Article
- A data-driven machine learning framework to predict side effects of AstraZeneca and sinopharm COVID-19 vaccines.Scientific reports · 2025Article
- Multimodal feature-optimized approaches for cancer classification using microarray gene expression analysis.Scientific reports · 2025Article
- Predicting cancer risk using machine learning on lifestyle and genetic data.Scientific reports · 2025Article
- Enhanced leukemia prediction using hybrid ant colony and ant lion optimization for gene selection and classification.MethodsX · 2025Article
- Identification of biomarkers associated with M1 macrophages in the ST-segment elevation myocardial infarction through bioinformatics and machine learning approaches.Scientific reports · 2025Article
- SGA-Driven feature selection and random forest classification for enhanced breast cancer diagnosis: A comparative study.Scientific reports · 2025Article
- Feature Selection in Breast Cancer Gene Expression Data Using KAO and AOA with SVM Classification.Journal of medical systems · 2025Article
- Advanced machine learning framework for enhancing breast cancer diagnostics through transcriptomic profiling.Discover oncology · 2025Article
- Risk prediction of hyperuricemia based on particle swarm fusion machine learning solely dependent on routine blood tests.BMC medical informatics and decision making · 2025Article
- Improving stroke risk prediction by integrating XGBoost, optimized principal component analysis, and explainable artificial intelligence.BMC medical informatics and decision making · 2025Article
- Secretary bird optimization algorithm based on quantum computing and multiple strategies improvement for KELM diabetes classification.Scientific reports · 2025Article
- A contribution-interpretable belief rule base model for obesity risk prediction using physical indicators and dietary habits.Frontiers in endocrinology · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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, including Central Nervous System (CNS), Leukemia, Breast, and Ovarian cancers, we demonstrate the efficacy of our method in identifying a smaller subset of unique and relevant genes. This selection results in significantly improved classification efficiency and accuracy. When compared with popular classifiers such as Support Vector Machine, K-Nearest Neighbor, and Naive Bayes, our approach consistently outperforms these models in terms of both accuracy and F-measure metrics. For instance, our framework achieved an accuracy of 97.24% in the CNS dataset, 99.14% in Leukemia, 95.21% in Ovarian, and 87.62% in Breast cancer, showcasing its robustness and effectiveness across different types of cancer data. These results underline the potential of our RDO-XGBoost framework as a promising solution for feature selection in cancer data analysis, offering enhanced predictive performance and valuable biological insights.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.