Optimal PCA-EOC-KNN Model for Detection of NS1 from Salivary SERS Spectra

• Main Authors: Othman, N. H., Lee, Khuan. Y., Radzol, A. R. M., Mansor, W., Rashid, U. R. M.
• Format: Conference Proceeding
• Language: English
• Subjects: Correlation; Diseases; Nearest Neighbour (kNN); Nonstructural Protein 1 (NS1); Principal component analysis; Principal Component Analysis (PCA); Proteins; Raman scattering; Robots; Training
• ISSN: 2189-8723
• DOI: 10.1109/ICIIBMS.2018.8549984

Non Structural Protein 1 (NS1) has recently been known as an alternative biomarker for diseases caused by flavivirus. It has been clinically acknowledged for early detection of dengue infection, since NS1 presence in blood can be as early as the first day of infection. Surface Enhanced Raman Spectroscopy (SERS) is an improvement to Raman spectroscopy, which amplifies the intensity of Raman scattering so to be usable. This also enables SERS to detect molecular structure up to a single molecule. As such, it is favorable amongst researchers investigating disease biomarker. Algorithm k-nearest neighbor (kNN) is a strategy to classify an unknown based on learning data, nearest to the class. Our work here intends to determine the optimal nearest neighbor number, distance rule and classifier rule for PCA-EOC-KNN model for automated detection of NS1 fingerprint from SERS spectra of adulterated saliva. Results show that PCA-EOC-KNN classifier performs with accuracy, precision, sensitivity and specificity above 90%, using Consensus classifier rule, Euclidean or Correlation or Cosine distance rule and k-value of 1, 3 and 5.