Khan Saranjam, Ullah Rahat, Khan Asifullah, Wahab Noorul, Bilal Muhammad, Ahmed Mushtaq
Agri-Biophotonics Division, National Institute for Lasers and Optronics (NILOP), Nilore, Islamabad 45650, Pakistan.
Pattern Recognition Lab, DCIS, Pakistan Institutes of Engineering and Applied Sciences (PIEAS), Nilore, Islamabad 45650, Pakistan.
Biomed Opt Express. 2016 May 18;7(6):2249-56. doi: 10.1364/BOE.7.002249. eCollection 2016 Jun 1.
The current study presents the use of Raman spectroscopy combined with support vector machine (SVM) for the classification of dengue suspected human blood sera. Raman spectra for 84 clinically dengue suspected patients acquired from Holy Family Hospital, Rawalpindi, Pakistan, have been used in this study.The spectral differences between dengue positive and normal sera have been exploited by using effective machine learning techniques. In this regard, SVM models built on the basis of three different kernel functions including Gaussian radial basis function (RBF), polynomial function and linear functionhave been employed to classify the human blood sera based on features obtained from Raman Spectra.The classification model have been evaluated with the 10-fold cross validation method. In the present study, the best performance has been achieved for the polynomial kernel of order 1. A diagnostic accuracy of about 85% with the precision of 90%, sensitivity of 73% and specificity of 93% has been achieved under these conditions.
本研究介绍了拉曼光谱结合支持向量机(SVM)用于登革热疑似人类血清分类的情况。本研究使用了从巴基斯坦拉瓦尔品第圣家族医院采集的84例临床登革热疑似患者的拉曼光谱。通过有效的机器学习技术利用了登革热阳性血清和正常血清之间的光谱差异。在这方面,基于包括高斯径向基函数(RBF)、多项式函数和线性函数在内的三种不同核函数构建的支持向量机模型已被用于根据从拉曼光谱获得的特征对人类血清进行分类。分类模型已采用10折交叉验证法进行评估。在本研究中,一阶多项式核取得了最佳性能。在这些条件下,实现了约85%的诊断准确率、90%的精确率、73%的灵敏度和93%的特异性。