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Hybrid feature selection and classification model using high-dimensional data based on a metaheuristic algorithm for brain cancer diagnosis
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  • Published: 03 March 2026

Hybrid feature selection and classification model using high-dimensional data based on a metaheuristic algorithm for brain cancer diagnosis

  • Ibrahim I. M. Manhrawy1,
  • Hanaa Fathi2,
  • Deema M. Alsekait3,
  • Arar Altawil4 &
  • …
  • Ayda K. Kelany5 

Scientific Reports , Article number:  (2026) Cite this article

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We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Biological techniques
  • Biophysics
  • Biotechnology
  • Cancer
  • Computational biology and bioinformatics

Abstract

Cancer is caused by somatic mutations, a dreadful disease that impacts individuals everywhere. Classifying gene expression data is essential for disease diagnosis and distinguishing tumor types. However, small sample sizes, numerous features, and noise make this task particularly challenging. This is especially true when performing feature selection on high-dimensional microarray data. It is critical to select the most pertinent and valuable genes from microarray data to identify prospective biomarkers or gain insight into the fundamental mechanisms of cancer. This study introduces a novel hybrid model that combines feature selection and classification to identify the most significant and informative features from microarray data associated with brain cancer. The research employs the GSE50161 dataset obtained from the Curated Microarray Database (CuMiDa), comprising 130 samples classified into five distinct categories with 54,676 genomes examined. We first applied mRMR to reduce dimensionality by removing redundant features, followed by HHO to refine the feature subset for optimal classification performance. To improve the performance of our model in classifying brain cancer microarray data, we utilized three metaheuristic algorithms: Differential Evolution (DE), Harris Hawks Optimization (HHO), and Particle Swarm Optimization (PSO). The hyperparameters “C” and “sigma” of the support vector machine (SVM) were optimized using these algorithms. The experimental results indicate that the suggested framework improves the capacity to differentiate between benign and malignant tissues with reduced time and dimensionality requirements. Furthermore, the genes selected for the dataset on brain cancer have undergone biological interpretation. This process is consistent with the findings of relevant scientific inquiries and significantly influences patients’ prognoses.

Data availability

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

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Acknowledgements

Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2026R435), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

Funding

Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2026R435), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

Author information

Authors and Affiliations

  1. Software Engineering Department, Faculty of Information Technology, Applied Science Private University, Amman, 11931, Jordan

    Ibrahim I. M. Manhrawy

  2. College of Computer Science and Informatics, Amman Arab University, Amman, Jordan

    Hanaa Fathi

  3. Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, 84428, 11671, Saudi Arabia

    Deema M. Alsekait

  4. Computer Science Department, Faculty of Information Technology, Applied Science Private University, Amman, 11931, Jordan

    Arar Altawil

  5. Department of Genomic Medicine, Cairo University, Giza, 12613, Egypt

    Ayda K. Kelany

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  1. Ibrahim I. M. Manhrawy
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Contributions

The authors confirm contribution to the paper as follows: study conception and design: HANAA FATHI, ARAR ALTAWIL, AYDA K. KELANY, IBRAHIM I. M. MANHRAWY; data collection: ARAR ALTAWIL, AYDA K. KELANY; analysis and interpretation of results: HANAA FATHI, ARAR ALTAWIL, AYDA K. KELANY, IBRAHIM I. M. MANHRAWY; draft manuscript preparation: DEEMA M. ALSEKAIT, HANAA FATHI, ARAR ALTAWIL, DEEMA M. ALSEKAIT, AYDA K. KELANY, IBRAHIM I. M. MANHRAWY. All authors reviewed the results and approved the final version of the manuscript.

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Correspondence to Ibrahim I. M. Manhrawy.

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Manhrawy, I.I.M., Fathi, H., Alsekait, D.M. et al. Hybrid feature selection and classification model using high-dimensional data based on a metaheuristic algorithm for brain cancer diagnosis. Sci Rep (2026). https://doi.org/10.1038/s41598-026-41573-5

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  • Received: 17 June 2025

  • Accepted: 20 February 2026

  • Published: 03 March 2026

  • DOI: https://doi.org/10.1038/s41598-026-41573-5

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Keywords

  • Feature selection-classification- optimization- metaheuristic algorithms
  • Harris Hawks optimization (HHO)
  • Particle Swarm Optimization (PSO)
  • Support vector machine (SVM)
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