Reduced Featured k-NN Classifier Model Optimal for Classification of Dengue Fever from Salivary Raman Spectra

Current diagnostic methods based on nonstructural protein 1 (NS1) for dengue infection use blood as the medium and hence are invasive. Worry for blood infected diseases, pain from pricking, overcrowded public hospitals and ignorance are just a few of the causes for delayed diagnosis that contributes to mortality from dengue fever (DF). NS1 has also been reported in saliva, but sensitivity of detection is much lower than that of blood. If saliva is to be a medium, detection of NS1 requires a more specific and sensitive technique. In this study, we are exploiting the advantages of saliva and Surface Enhanced Raman Spectroscopy (SERS) to develop a non-invasive early detection method for DF. Significant features from Raman spectra of saliva samples of dengue suspected patients and healthy volunteers were extracted with Principal Component Analysis (PCA) and served as input to k-Nearest Neighbour (k-NN) for classification. Cumulative Percentage Variance (CPV) is the criterion for feature extraction. Two k-NN distance rules (Cosine and Manhattan) combined with k-values ranging from 3 to 17 were varied to obtain an optimal k-NN classifier. Then, performance of the different k-NN classifier models is benchmarked against Panbio Dengue Early ELISA and SD BIOLINE Dengue Duo technique from the clinical laboratory. The finding is encouraging with the best performance achieved, 82.14% for accuracy, 85.71% for sensitivity and 78.57% for specificity.