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The Diagnosis of Dengue Virus Using Principal Component Analysis and Random Forest Based on Raman Spectroscopy
Aldo Novaznursyah Costrada, Nina Siti Aminah, Dessy Natalia, Mitra Djamal

Bandung Institute of Technology


Abstract

The diagnosis of dengue virus based on Raman spectra of dengue virus infected and non-dengue blood serum has been developed and proposed in this study. Raman spectral data from dengue virus infected blood serum samples were used for classification using Principal Component Analysis (PCA) combined with Random Forest (RF) classifier. Experimental and quantitative analysis is based on blood serum samples that have been diagnosed with dengue virus through NS1 and IgG/IgM testing. The ratio of training data and test data used in this model was 8:2 of the total data. The Iterations of 50 times and estimators of 100 decision trees were applied to each model. The proposed technique has shown good potential to be used in the differentiation between dengue and non-dengue virus infected sera. PCA-RF technique yielded 95.89% accuracy while RF showed 89.05% accuracy.

Keywords: Dengue Virus, PCA, Random Forest, Raman Spectroscopy

Topic: Physics

Plain Format | Corresponding Author (Aldo Novaznursyah Costrada)

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