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Issue 12, 2018
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An improved k-nearest neighbour method to diagnose breast cancer

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Abstract

As a molecular and noninvasive detection technology, Raman spectroscopy is promising for use in the early diagnosis of tumors. The SNR of spectra obtained from portable Raman spectrometers is low, which makes classification more difficult. A classification algorithm with a high recognition rate is required. In this paper, an algorithm of entropy weighted local-hyperplane k-nearest-neighbor (EWHK) is proposed for the identification of the spectra. When calculating the weighted distance between the prediction and the sample hyperplane, EWHK introduces the information entropy weighting to improve the algorithm of adaptive weighted k-local hyperplane (AWKH). It can reflect all of the sample information in the classification objectively and improve the classification accuracy. The breast cancer detection experimental results of EWHK showed a significant improvement compared with those of AWKH and k-nearest neighbor (KNN). The EWHK classifier yielded an average diagnostic accuracy of 92.33%, a sensitivity of 93.81%, a specificity of 87.77%, a positive prediction rate of 95.99% and a negative prediction rate of 83.69% during randomized grouping validation. The algorithm is effective for cancer diagnosis.

Graphical abstract: An improved k-nearest neighbour method to diagnose breast cancer

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Publication details

The article was received on 31 Jan 2018, accepted on 13 May 2018 and first published on 23 May 2018


Article type: Paper
DOI: 10.1039/C8AN00189H
Citation: Analyst, 2018,143, 2807-2811
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    An improved k-nearest neighbour method to diagnose breast cancer

    Q. Li, W. Li, J. Zhang and Z. Xu, Analyst, 2018, 143, 2807
    DOI: 10.1039/C8AN00189H

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