Chemometrics analysis for the detection of dental caries via ultraviolet absorption spectroscopy / Katrul Nadia Basri

The prevalence of dental caries is still high and this has raised a major concern to the society and government. Dental caries is a progressive disease that belongs to the group of non-communicable diseases (NCDs). Currently, dental caries has become the first ranking among the NCDs due to its high...

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Main Author:	Basri, Katrul Nadia
Format:	Thesis
Language:	English
Published:	2023
Online Access:	https://ir.uitm.edu.my/id/eprint/88906/1/88906.pdf
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spelling	my-uitm-ir.889062024-01-03T08:15:32Z Chemometrics analysis for the detection of dental caries via ultraviolet absorption spectroscopy / Katrul Nadia Basri 2023 Basri, Katrul Nadia The prevalence of dental caries is still high and this has raised a major concern to the society and government. Dental caries is a progressive disease that belongs to the group of non-communicable diseases (NCDs). Currently, dental caries has become the first ranking among the NCDs due to its high prevalence. Methods such as visual inspection by the dentist, imaging and illumination techniques have been widely used by the dentist to diagnose the patient’s severity of dental caries. These conventional methods required expert assistance, reagent and were mostly used for diagnostic purpose. Ultraviolet (UV) spectroscopy has a great potential to be an early screening tool for the detection of dental caries. Chemometrics analysis needs to be coupled with UV spectroscopy to correlate with the UV spectra and the caries score based on International Caries Detection and Assessment System (ICDAS). The UV spectra collected in the range 200 – 350 nm shows the absorption at 260-310 nm due to the presence of certain bacteria that cause dental caries. The spectra were split into calibration and validation data using stratified random sampling for each of ICDAS class by a ratio of 80:20. Different preprocessing methods (mean centre, autoscale and Savitzky-Golay smoothing) were applied to the spectra to reduce the noise embedded in the spectra. Classification algorithms such as K-nearest neighbour (KNN), logistic regression (LR), linear discriminant analysis (LDA) and decision tree (DT) were employed to classify the spectra into its ICDAS score. The best performance obtained using Savitzky-Golay smoothing and LDA algorithms after the wavelength selection with the accuracy reported of 0.90. The precision, sensitivity, specificity obtained for the model were 1.00, 0.86 and 1.00 respectively. Artificial neural network (ANN) was performed on the spectra to investigate its feasibility to predict the dental caries. The ANN architecture was optimized by tuning the hyperparameter. The best result of ANN model obtained were 0.85, 0.8, 0.57 and 0.92 for accuracy, precision, sensitivity and specificity. Dimension reduction algorithm such as LDA and CNN were applied on the spectra to reduce the number of variables to be trained. The result obtained has revealed that the combination of LDA-ANN did not improve the performance of the model. The accuracy obtained using CNN was 0.85 for the overall performance of calibration and validation. Model 2 of CNN has maximum performance of validation in terms of accuracy, precision, sensitivity and specificity but the calibration model requires more optimization. The accuracy of the CNN model is comparable with the accuracy of the previous work that utilizing CNN for the imaging data to detect caries (diagnostic tool). 2023 Thesis https://ir.uitm.edu.my/id/eprint/88906/ https://ir.uitm.edu.my/id/eprint/88906/1/88906.pdf text en public phd doctoral Universiti Teknologi MARA (UiTM) College of Engineering Sabirin, Ahmad
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Sabirin, Ahmad
description	The prevalence of dental caries is still high and this has raised a major concern to the society and government. Dental caries is a progressive disease that belongs to the group of non-communicable diseases (NCDs). Currently, dental caries has become the first ranking among the NCDs due to its high prevalence. Methods such as visual inspection by the dentist, imaging and illumination techniques have been widely used by the dentist to diagnose the patient’s severity of dental caries. These conventional methods required expert assistance, reagent and were mostly used for diagnostic purpose. Ultraviolet (UV) spectroscopy has a great potential to be an early screening tool for the detection of dental caries. Chemometrics analysis needs to be coupled with UV spectroscopy to correlate with the UV spectra and the caries score based on International Caries Detection and Assessment System (ICDAS). The UV spectra collected in the range 200 – 350 nm shows the absorption at 260-310 nm due to the presence of certain bacteria that cause dental caries. The spectra were split into calibration and validation data using stratified random sampling for each of ICDAS class by a ratio of 80:20. Different preprocessing methods (mean centre, autoscale and Savitzky-Golay smoothing) were applied to the spectra to reduce the noise embedded in the spectra. Classification algorithms such as K-nearest neighbour (KNN), logistic regression (LR), linear discriminant analysis (LDA) and decision tree (DT) were employed to classify the spectra into its ICDAS score. The best performance obtained using Savitzky-Golay smoothing and LDA algorithms after the wavelength selection with the accuracy reported of 0.90. The precision, sensitivity, specificity obtained for the model were 1.00, 0.86 and 1.00 respectively. Artificial neural network (ANN) was performed on the spectra to investigate its feasibility to predict the dental caries. The ANN architecture was optimized by tuning the hyperparameter. The best result of ANN model obtained were 0.85, 0.8, 0.57 and 0.92 for accuracy, precision, sensitivity and specificity. Dimension reduction algorithm such as LDA and CNN were applied on the spectra to reduce the number of variables to be trained. The result obtained has revealed that the combination of LDA-ANN did not improve the performance of the model. The accuracy obtained using CNN was 0.85 for the overall performance of calibration and validation. Model 2 of CNN has maximum performance of validation in terms of accuracy, precision, sensitivity and specificity but the calibration model requires more optimization. The accuracy of the CNN model is comparable with the accuracy of the previous work that utilizing CNN for the imaging data to detect caries (diagnostic tool).
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Basri, Katrul Nadia
spellingShingle	Basri, Katrul Nadia Chemometrics analysis for the detection of dental caries via ultraviolet absorption spectroscopy / Katrul Nadia Basri
author_facet	Basri, Katrul Nadia
author_sort	Basri, Katrul Nadia
title	Chemometrics analysis for the detection of dental caries via ultraviolet absorption spectroscopy / Katrul Nadia Basri
title_short	Chemometrics analysis for the detection of dental caries via ultraviolet absorption spectroscopy / Katrul Nadia Basri
title_full	Chemometrics analysis for the detection of dental caries via ultraviolet absorption spectroscopy / Katrul Nadia Basri
title_fullStr	Chemometrics analysis for the detection of dental caries via ultraviolet absorption spectroscopy / Katrul Nadia Basri
title_full_unstemmed	Chemometrics analysis for the detection of dental caries via ultraviolet absorption spectroscopy / Katrul Nadia Basri
title_sort	chemometrics analysis for the detection of dental caries via ultraviolet absorption spectroscopy / katrul nadia basri
granting_institution	Universiti Teknologi MARA (UiTM)
granting_department	College of Engineering
publishDate	2023
url	https://ir.uitm.edu.my/id/eprint/88906/1/88906.pdf
_version_	1794192174218215424

Chemometrics analysis for the detection of dental caries via ultraviolet absorption spectroscopy / Katrul Nadia Basri

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