PCA-QDA model selection for detecting NS1 related diseases from SERS spectra of salivary mixtures

N. H. Othman, Yoot Khuan Lee, A. R.M. Radzol, Wahidah Mansor, N. I.A. Hisham

Research output: Contribution to journalConference article

Abstract

Of recent, non-structural protein (NS1) in saliva has emerged to be engaging as a detection biomarker for diseases related to NS1 at febrile stage. Non-invasive detection of NS1 in saliva, free from risk of blood infection, further will make the approach more preferred than the current serum based ones. Our work here intends to define an optimal classifier model for Quadratic Discriminant Analysis (QDA), optimized with Principal Component Analysis (PCA), to distinct between positive and negative NS1 adulterated samples from salivary SERS spectra. The adulterated samples are acquired from our UiTM-NMRR-12-1278-12868-NS1-DENV database. Then, PCA extracts significant features from the database after pre-processing, based on three stopping criteria, which are served as inputs to the QDA classifiers. It is found that the PCA-QDA pseudo model with 5, 70 and 115 principal components from the three criterion achieves performance of 100% (Scree), 84.2% (CPV) and 55.3% (EOC) in accuracy. Higher accuracy at 100% (Scree), 97.3684% (CPV) and 97.3684% (EOC) are observed with QDA diagonal model.

LanguageEnglish
Pages623-627
Number of pages5
JournalIFMBE Proceedings
Volume68
Issue number1
DOIs
Publication statusPublished - Jan 1 2019
EventWorld Congress on Medical Physics and Biomedical Engineering, WC 2018 - Prague, Czech Republic
Duration: Jun 3 2018Jun 8 2018

Fingerprint

Discriminant analysis
Principal component analysis
Classifiers
Biomarkers
Blood
Proteins
Processing

All Science Journal Classification (ASJC) codes

  • Bioengineering
  • Biomedical Engineering

Cite this

PCA-QDA model selection for detecting NS1 related diseases from SERS spectra of salivary mixtures. / Othman, N. H.; Lee, Yoot Khuan; Radzol, A. R.M.; Mansor, Wahidah; Hisham, N. I.A.

In: IFMBE Proceedings, Vol. 68, No. 1, 01.01.2019, p. 623-627.

Research output: Contribution to journalConference article

@article{9af3e312a63e4de4b69f225ca2e5aa1d,
title = "PCA-QDA model selection for detecting NS1 related diseases from SERS spectra of salivary mixtures",
abstract = "Of recent, non-structural protein (NS1) in saliva has emerged to be engaging as a detection biomarker for diseases related to NS1 at febrile stage. Non-invasive detection of NS1 in saliva, free from risk of blood infection, further will make the approach more preferred than the current serum based ones. Our work here intends to define an optimal classifier model for Quadratic Discriminant Analysis (QDA), optimized with Principal Component Analysis (PCA), to distinct between positive and negative NS1 adulterated samples from salivary SERS spectra. The adulterated samples are acquired from our UiTM-NMRR-12-1278-12868-NS1-DENV database. Then, PCA extracts significant features from the database after pre-processing, based on three stopping criteria, which are served as inputs to the QDA classifiers. It is found that the PCA-QDA pseudo model with 5, 70 and 115 principal components from the three criterion achieves performance of 100{\%} (Scree), 84.2{\%} (CPV) and 55.3{\%} (EOC) in accuracy. Higher accuracy at 100{\%} (Scree), 97.3684{\%} (CPV) and 97.3684{\%} (EOC) are observed with QDA diagonal model.",
author = "Othman, {N. H.} and Lee, {Yoot Khuan} and Radzol, {A. R.M.} and Wahidah Mansor and Hisham, {N. I.A.}",
year = "2019",
month = "1",
day = "1",
doi = "10.1007/978-981-10-9035-6_116",
language = "English",
volume = "68",
pages = "623--627",
journal = "IFMBE Proceedings",
issn = "1680-0737",
publisher = "Springer Verlag",
number = "1",

}

TY - JOUR

T1 - PCA-QDA model selection for detecting NS1 related diseases from SERS spectra of salivary mixtures

AU - Othman, N. H.

AU - Lee, Yoot Khuan

AU - Radzol, A. R.M.

AU - Mansor, Wahidah

AU - Hisham, N. I.A.

PY - 2019/1/1

Y1 - 2019/1/1

N2 - Of recent, non-structural protein (NS1) in saliva has emerged to be engaging as a detection biomarker for diseases related to NS1 at febrile stage. Non-invasive detection of NS1 in saliva, free from risk of blood infection, further will make the approach more preferred than the current serum based ones. Our work here intends to define an optimal classifier model for Quadratic Discriminant Analysis (QDA), optimized with Principal Component Analysis (PCA), to distinct between positive and negative NS1 adulterated samples from salivary SERS spectra. The adulterated samples are acquired from our UiTM-NMRR-12-1278-12868-NS1-DENV database. Then, PCA extracts significant features from the database after pre-processing, based on three stopping criteria, which are served as inputs to the QDA classifiers. It is found that the PCA-QDA pseudo model with 5, 70 and 115 principal components from the three criterion achieves performance of 100% (Scree), 84.2% (CPV) and 55.3% (EOC) in accuracy. Higher accuracy at 100% (Scree), 97.3684% (CPV) and 97.3684% (EOC) are observed with QDA diagonal model.

AB - Of recent, non-structural protein (NS1) in saliva has emerged to be engaging as a detection biomarker for diseases related to NS1 at febrile stage. Non-invasive detection of NS1 in saliva, free from risk of blood infection, further will make the approach more preferred than the current serum based ones. Our work here intends to define an optimal classifier model for Quadratic Discriminant Analysis (QDA), optimized with Principal Component Analysis (PCA), to distinct between positive and negative NS1 adulterated samples from salivary SERS spectra. The adulterated samples are acquired from our UiTM-NMRR-12-1278-12868-NS1-DENV database. Then, PCA extracts significant features from the database after pre-processing, based on three stopping criteria, which are served as inputs to the QDA classifiers. It is found that the PCA-QDA pseudo model with 5, 70 and 115 principal components from the three criterion achieves performance of 100% (Scree), 84.2% (CPV) and 55.3% (EOC) in accuracy. Higher accuracy at 100% (Scree), 97.3684% (CPV) and 97.3684% (EOC) are observed with QDA diagonal model.

UR - http://www.scopus.com/inward/record.url?scp=85048258849&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85048258849&partnerID=8YFLogxK

U2 - 10.1007/978-981-10-9035-6_116

DO - 10.1007/978-981-10-9035-6_116

M3 - Conference article

VL - 68

SP - 623

EP - 627

JO - IFMBE Proceedings

T2 - IFMBE Proceedings

JF - IFMBE Proceedings

SN - 1680-0737

IS - 1

ER -