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

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    This study introduces a non-invasive method for early dengue diagnosis using saliva and Surface Enhanced Raman Spectroscopy (SERS). The technique shows promising accuracy and sensitivity for detecting dengue infection, potentially reducing mortality rates.

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    Area of Science:

    • Biomedical diagnostics
    • Spectroscopy
    • Infectious disease detection

    Background:

    • Current dengue diagnosis relies on invasive blood tests using nonstructural protein 1 (NS1).
    • Delayed diagnosis due to invasive methods contributes to dengue fever (DF) mortality.
    • NS1 detection in saliva is less sensitive, requiring advanced techniques for effective diagnosis.

    Purpose of the Study:

    • To develop a non-invasive early detection method for dengue infection using saliva.
    • To leverage Surface Enhanced Raman Spectroscopy (SERS) for enhanced NS1 detection in saliva.
    • To establish a sensitive and specific diagnostic approach for dengue.

    Main Methods:

    • Saliva samples from dengue-suspected patients and healthy volunteers were analyzed using SERS.
    • Principal Component Analysis (PCA) extracted significant spectral features for classification.
    • k-Nearest Neighbour (k-NN) algorithms with varying distance rules and k-values were employed for classification.
    • Performance was benchmarked against commercial ELISA and Duo techniques.

    Main Results:

    • The developed SERS-based method achieved 82.14% accuracy.
    • Sensitivity reached 85.71%, and specificity was 78.57% in classifying dengue cases.
    • The approach demonstrated effectiveness in differentiating between dengue-suspected and healthy individuals.

    Conclusions:

    • Surface Enhanced Raman Spectroscopy (SERS) combined with PCA and k-NN offers a viable non-invasive method for early dengue detection.
    • This saliva-based approach shows potential for improving diagnostic accessibility and reducing DF mortality.
    • Further refinement could lead to a clinically applicable, sensitive, and specific dengue diagnostic tool.