ArticleJournal of medical systems2025
Feature Selection in Breast Cancer Gene Expression Data Using KAO and AOA with SVM Classification.
Article in Journal of medical systems, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Feature selection methodology based on explainable AI: application in predicting extubation success in the intensive care unit.Scientific reports · 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
- 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
Corrections and comments
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Authors and funding
2 authors.
Funding
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Abstract
Breast cancer classification using gene expression data presents significant challenges due to high dimensionality and complexity. This study introduces a novel hybrid framework integrating the Kashmiri Apple Optimization Algorithm (KAO) and the Armadillo Optimization Algorithm (AOA) for effective feature selection, coupled with Support Vector Machines (SVM) for precise classification. The dual-stage approach leverages KAO for global exploration of informative genes and AOA for refining the selection through local optimization, addressing issues of redundancy and premature convergence. Applied to breast cancer datasets, the proposed method achieved a classification accuracy of 98.97%, precision of 98.46%, recall of 100%, and an F1-score of 99.22% using a subset of 15 genes. The robustness of the framework was validated across varying subset sizes, demonstrating consistent high performance. By optimizing feature relevance and redundancy, the KAO-AOA framework provides a promising tool for gene-based cancer prediction with potential applications to other cancer datasets and real-world clinical use.
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Registered trials
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 Socratic graph.