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PCA-SCG-ANN for Detection of Non-structural Protein 1 from SERS Salivary Spectra

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Intelligent Information and Database Systems (ACIIDS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10192))

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Abstract

With non-structural protein (NS1) being acknowledged as biomarker for Dengue fever, the need to automate detection of NS1 from salivary surface enhanced Raman spectroscopic (SERS) spectra, with claim of sensitivity up to a single molecule thus become eminent. Choice for Principal Component Analysis (PCA) termination criterion and artificial neural network (ANN) topology critically affect the performance and efficiency of PCA-SCG-ANN classifier. This paper aims to explore the effect of number of hidden node for the ANN topology and PCA termination criterion on the performance of the PCA-SCG-ANN classifier for detection of NS1 from SERS spectra of saliva of subjects. The Eigenvalue-One-Criterion (EOC), Cumulative Percentage Variance (CPV) and Scree criteria, integrated with ANN topology containing hidden nodes from 3 to 100 are investigated. Performance of a total of 42 classifier models are examined and compared in terms of accuracy, precision, sensitivity. From experiments, it is found that EOC criterion paired with ANN topology of 13 hidden node outperforms the other models, with a performance of [Accuracy 91%, Precision 94%, Sensitivity 94%, Specificity 96%].

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Acknowledgment

The author would like to thank the Ministry of Science and Technology (MOSTI), Malaysia, for providing the research funding 100-RMI/SF 16/6/2 (14/2015); the Research Management Institute, Universiti Teknologi MARA, Malaysia and the Faculty of Electrical Engineering, Universiti Teknologi MARA, Malaysia, for the support and assistance given to the authors in carrying out this research; Ministry of Health, Selangor (JKNS) for their assistance and permission in providing subjects and advice. The experimental procedures involving human subjects described in this paper were approved by the Medical Research & Ethics Committee, National Medical Research Register (NMRR 12868), Malaysia.

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Authors and Affiliations

  1. Faculty of Electrical Engineering, Universiti Teknologi MARA, Shah Alam, Selangor DE, Malaysia

    N. H. Othman, Khuan Y. Lee, A. R. M. Radzol & W. Mansor

  2. Computational Intelligence Detection RIG, Pharmaceutical and Lifesciences Communities of Research, Universiti Teknologi MARA, Shah Alam, Selangor DE, Malaysia

    Khuan Y. Lee, A. R. M. Radzol & W. Mansor

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  1. N. H. Othman
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  2. Khuan Y. Lee
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  3. A. R. M. Radzol
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  4. W. Mansor
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Correspondence to Khuan Y. Lee .

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Editors and Affiliations

  1. Wrocław University of Science and Technology, Wroclaw, Poland

    Ngoc Thanh Nguyen

  2. Japan Advanced Institute of Science and Technology, Nomi, Japan

    Satoshi Tojo

  3. Japan Advanced Institute of Science and Technology, Nomi, Japan

    Le Minh Nguyen

  4. Wrocław University of Science and Technology, Wroclaw, Poland

    Bogdan Trawiński

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Othman, N.H., Lee, K.Y., Radzol, A.R.M., Mansor, W. (2017). PCA-SCG-ANN for Detection of Non-structural Protein 1 from SERS Salivary Spectra. In: Nguyen, N., Tojo, S., Nguyen, L., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2017. Lecture Notes in Computer Science(), vol 10192. Springer, Cham. https://doi.org/10.1007/978-3-319-54430-4_41

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  • DOI: https://doi.org/10.1007/978-3-319-54430-4_41

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-54430-4

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