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Bio-Inspired ensemble feature selection and deep auto-encoder approach for rapid diagnosis of breast cancer

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Abstract

In the modern era, breast cancer (BC) is one of the most prevalent diseases affecting the lifespan of women. Single nucleotide polymorphism (SNP) elucidates an enormous proportion of the hazard in women with a solid family history. Different types of human disorders have been analyzed using Machine Learning methods to locate the vital SNP. The identification of an optimal feature set is the primary constraint in the existing methods owing to the ill effects of multidimensionality. Thus, a novel Bio-Inspired Ensemble Feature Selection (BIEFS) technique has been proposed in this paper to identify the most relevant SNP for accurate classification of BC. An initial feature subset is generated from each base feature selector such as Membership Weight Salp Swarm Algorithm (MWSSA), Crossover Horse Herd Optimization (CHHO), and Levy Mutation Manta-Ray Foraging Optimization (LMMRFO). Then the proposed BIEFS technique obtains the optimized weight of each feature subset through the mutation operator. Finally, the Self-Organizing Deep Auto-Encoder (SODAE) is employed for BC classification. A Gene Expression Omnibus (GEO) dataset is used to assess the proposed methodology. Simulation results validate that the proposed methodology attains a maximum accuracy of 98.75% as compared to the conventional techniques.

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Availability of data and materials

The datasets analyzed during the current study are available in [26].

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Funding

The authors did not receive support from any organization for the submitted work.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Dr. N. G. P. Institute of Technology, Coimbatore, Tamil Nadu, India

    V. Praveena & L. R. Sujithra

  2. Department of Computer Science and Engineering, SNS College of Technology, Coimbatore, Tamil Nadu, India

    S. Karthik & M. S. Kavitha

Authors
  1. V. Praveena
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  2. L. R. Sujithra
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  3. S. Karthik
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  4. M. S. Kavitha
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Contributions

VP: conceptualization, visualization, LRS: software, writing—original draft preparation. SK: validation, investigation, software. MSK: methodology.

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Correspondence to L. R. Sujithra.

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This material is the authors' own original work, which has not been previously published elsewhere. The paper is not currently being considered for publication elsewhere. The paper reflects the authors' own research and analysis in a truthful and complete manner.

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Communicated by B. Xiao.

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Praveena, V., Sujithra, L.R., Karthik, S. et al. Bio-Inspired ensemble feature selection and deep auto-encoder approach for rapid diagnosis of breast cancer. Multimedia Systems 29, 3403–3419 (2023). https://doi.org/10.1007/s00530-023-01168-w

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  • DOI: https://doi.org/10.1007/s00530-023-01168-w

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