1 Agri-biophotonics Laboratory, National Institute for Lasers & Optronics, Islamabad, Pakistan.
2 Department of Computer and Informatics Sciences, Pakistan Institutes of Engineering and Applied Sciences, Islamabad, Pakistan.
Appl Spectrosc. 2017 Sep;71(9):2111-2117. doi: 10.1177/0003702817695571. Epub 2017 Mar 17.
This work presents the evaluation of Raman spectroscopy using random forest (RF) for the analysis of dengue fever in the infected human sera. A total of 100 dengue suspected blood samples, collected from Holy Family Hospital, Rawalpindi, Pakistan, have been used in this study. Out of these samples, 45 were dengue-positive based on immunoglobulin M (IgM) capture enzyme-linked immunosorbent assay (ELISA) tests. For highlighting the spectral differences between normal and infected samples, an effective machine learning system is developed that automatically learns the pattern of the shift in spectrum for the dengue compared to normal cases and thus is able to predict the unknown class based on the known example. In this connection, dimensionality reduction has been performed with the principal component analysis (PCA), while RF is used for automatic classification of dengue samples. For the determination of diagnostic capabilities of Raman spectroscopy based on RF, sensitivity, specificity, and accuracy have been calculated in comparison to normally performed IgM capture ELISA. According to the experiment, accuracy of 91%, sensitivity of 91%, and specificity of 91% were achieved for the proposed RF-based model.
本工作评估了随机森林（RF）在分析感染人类血清中的登革热中的应用。这项研究共使用了 100 份来自巴基斯坦拉瓦尔品第圣家族医院的疑似登革热血液样本。其中，45 份样本的免疫球蛋白 M（IgM）捕获酶联免疫吸附试验（ELISA）结果呈阳性。为了突出正常和感染样本之间的光谱差异，开发了一种有效的机器学习系统，该系统能够自动学习登革热与正常病例之间光谱偏移的模式，从而能够根据已知示例预测未知类别。在这方面，采用主成分分析（PCA）进行了降维，同时采用 RF 对登革热样本进行自动分类。为了确定基于 RF 的拉曼光谱的诊断能力，与通常进行的 IgM 捕获 ELISA 相比，计算了灵敏度、特异性和准确性。根据实验，基于 RF 的模型的准确性为 91%，灵敏度为 91%，特异性为 91%。
