School of Instrumentation Science and Opto-Electronics Engineering, Precision Opto-Mechatronics Technology Key Laboratory of Education Ministry, Beihang University, Xueyuan Road No. 37, Haidian District, Beijing, 100191, China.
Analyst. 2018 Jun 11;143(12):2807-2811. doi: 10.1039/c8an00189h.
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.
作为一种分子和非侵入式检测技术，拉曼光谱在肿瘤的早期诊断中具有广阔的应用前景。便携式拉曼光谱仪获得的光谱信噪比低，这使得分类更加困难。需要一种具有高识别率的分类算法。本文提出了一种基于熵加权局部超平面 k-最近邻（EWHK）的算法，用于识别光谱。在计算预测与样本超平面之间的加权距离时，EWHK 引入了信息熵加权，以改进自适应加权 k-局部超平面（AWKH）算法。它可以客观地反映分类中的所有样本信息，提高分类精度。EWHK 的乳腺癌检测实验结果与 AWKH 和 k-最近邻（KNN）相比有显著提高。在随机分组验证中，EWHK 分类器的平均诊断准确率为 92.33%，灵敏度为 93.81%，特异性为 87.77%，阳性预测率为 95.99%，阴性预测率为 83.69%。该算法对癌症诊断有效。
