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An entropy-based classification of breast cancerous genes using microarray data

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Abstract

Gene expression levels obtained from microarray data provide a promising technique for doing classification on cancerous data. Due to the high dimensionality of the microarray datasets, the redundant genes need to be removed and only significant genes are required for building the classifier. In this work, an entropy-based method was used based on supervised learning to differentiate between normal tissue and breast tumor based on their gene expression profiles. This work employs four widely used machine learning techniques for breast cancer prediction, namely support vector machine (SVM), random forest, k-nearest neighbor (KNN) and naive Bayes. The performance of these techniques was evaluated on four different classification performance measurements which result in getting more accuracy in case of SVM as compared to other machine learning algorithms. Classification accuracy of 91.5% was achieved by support vector machine with 0.833 F1 measures. Furthermore, these techniques were evaluated on the basis of performance by ROC curve and calibration graph.

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Acknowledgements

The authors acknowledge the Department of Bioinformatics and Applied Sciences, Indian Institute of Information Technology, Allahabad, for providing computing facility.

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Author notes

  1. Mausami Mondal, Rahul Semwal, Utkarsh Raj, and Imlimaong Aier have contributed equally to this work.

Authors and Affiliations

  1. Department of Bioinformatics and Applied Sciences, Indian Institute of Information Technology - Allahabad, Prayagraj, 211015, India

    Mausami Mondal, Rahul Semwal, Utkarsh Raj, Imlimaong Aier & Pritish Kumar Varadwaj

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  1. Mausami Mondal
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  2. Rahul Semwal
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  3. Utkarsh Raj
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  4. Imlimaong Aier
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  5. Pritish Kumar Varadwaj
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Correspondence to Pritish Kumar Varadwaj.

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Mondal, M., Semwal, R., Raj, U. et al. An entropy-based classification of breast cancerous genes using microarray data. Neural Comput & Applic 32, 2397–2404 (2020). https://doi.org/10.1007/s00521-018-3864-8

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  • DOI: https://doi.org/10.1007/s00521-018-3864-8

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