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Combining the mRMR technique with the Northern Goshawk Algorithm (NGHA) to choose genes for cancer classification

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Abstract

This paper presents a novel strategy for gene selection in cancer classification by integrating the Northern Goshawk Algorithm (NGHA) with microarray datasets. The primary objective is to enhance the precision of cancer diagnosis by efficiently identifying informative genes within the often-noisy nature of these datasets. The proposed method consists of two key stages: the filter stage, employing the Minimum Redundancy Maximum Relevancy (mRMR) method, and the wrapper stage, harnessing the synergies of NGHA and Support Vector Machine (SVM) classifier. Experimental assessments on two microarray datasets demonstrate the method's accuracy and effectiveness. Comparative evaluations against common gene selection techniques indicate comparable performance on several datasets, with particularly promising and novel results on two datasets. The innovative integration of mRMR and the Northern Goshawk Algorithm presents a potent strategy for advancing gene selection in cancer classification, holding significant potential for elevating diagnostic precision in oncology.

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The data used in this research project is publicly available.

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Authors and Affiliations

  1. Department of Mathematics, VIT Bhopal University, Kothrikalan Sehore, Sehore, MP, 466114, India

    Abrar Yaqoob

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  1. Abrar Yaqoob
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Contributions

Abrar Yaqoob played a pivotal role in data analysis, drawing meaningful insights from the gathered information. Their meticulous attention to detail and analytical skills greatly enriched the project. Furthermore, the author asserts that there are no conflicts of interest to declare, ensuring the integrity and impartiality of the research findings.

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Correspondence to Abrar Yaqoob.

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Yaqoob, A. Combining the mRMR technique with the Northern Goshawk Algorithm (NGHA) to choose genes for cancer classification. Int. j. inf. tecnol. (2024). https://doi.org/10.1007/s41870-024-01849-3

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