Feature Selection in Breast Cancer Gene Expression Data Using KAO and AOA with SVM Classification.
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 f...
| Publicado en: | Journal of Medical Systems Vol. 49; no. 1; pp. 1 - 22 |
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| Autores principales: | , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
3/26/2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=184039200&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 184039200 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: 3/26/2025 vid: 49 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 184039200 184039200 184039200 10.1007/s10916-025-02171-6 184039200 ppf: 1 ppct: 21 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Feature Selection in Breast Cancer Gene Expression Data Using KAO and AOA with SVM Classification. aug: au: Yaqoob, Abrar Verma, Navneet Kumar affil: https://ror.org/02ax13658 School of Advanced Sciences and Languages, VIT Bhopal University, 466114, Sehore Bhopal, India sug: subj: Breast Neoplasms Classification Breast Neoplasms Familial and Genetic Gene Expression Profiling Support Vector Machine Algorithms Human Conceptual Framework Machine Learning Tumor Markers, Biological Prediction Models Bioinformatics ab: 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. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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