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Volume 18, Issue 3 (Iranian Journal of Breast Diseases 2025)
ijbd 2025, 18(3): 112-0 |
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motevali alamuti M, khalilian M, bastanfard A. Breast cancer prediction approach based on microarray data using hybrid gene selection and deep learning. ijbd 2025; 18 (3) :112-0
URL: http://ijbd.ir/article-1-1154-en.html
URL: http://ijbd.ir/article-1-1154-en.html
1- Ph.D. Candidate, Department of Computer Engineering, Ka.C, Islamic Azad University, Karaj, Iran
2- Department of Computer Engineering, Ka.C, Islamic Azad University, Karaj, Iran ,Khalilian@kiau.ac.ir
3- Department of Computer Engineering, Ka.C, Islamic Azad University, Karaj, Iran
2- Department of Computer Engineering, Ka.C, Islamic Azad University, Karaj, Iran ,
3- Department of Computer Engineering, Ka.C, Islamic Azad University, Karaj, Iran
Abstract: (253 Views)
Introduction: Breast cancer is the most common malignancy among women and the second leading cause of cancer mortality. Gene expression analysis using microarray data reveals molecular patterns associated with disease progression, aiding in diagnosis and treatment. However, the high dimensionality of such data poses significant challenges for machine learning methods.
Methods: Breast cancer is the most common malignancy in women and a major cause of cancer mortality. This study introduces a hybrid feature selection method combining filter, wrapper, FWGPC, and deep learning to handle high-dimensional microarray data. The approach enhances classification accuracy and identifies key genes linked to breast cancer.
Results: Experimental results demonstrate that the proposed method achieves classification accuracies of 99.96% and 96.1% on the BC-TCGA and GSE datasets, respectively. It outperforms many other classification approaches in identifying new breast cancer cases.
Conclusion: This study applies the FWGPC algorithm by combining filter and wrapper approaches for feature selection in breast cancer microarray data. The best outcomes compete, and classification accuracy is evaluated through deep learning. The approach enables key gene identification and improves classification performance.
Keywords: Breast Cancer, Microarray Data, Feature Selection, Giza Pyramids Construction Algorithm, Deep Learning.
Type of Study: Applicable |
Subject:
prevention
Received: 2024/11/11 | Accepted: 2025/09/3 | Published: 2025/10/2
Received: 2024/11/11 | Accepted: 2025/09/3 | Published: 2025/10/2
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