| Summary: | Caries is one of the non-communicable diseases that has a high prevalence trend. The current methods used to detect caries require sophisticated laboratory equipment, professional inspection, and expensive equipment such as X-ray imaging device. A non-invasive and economical method is required to substitute the conventional methods for the detection of caries. UV absorption spectroscopy coupled with chemometrics analysis has emerged as a good potential candidate for such an application. Data preprocessing methods such as mean centre, autoscale and Savitzky-Golay smoothing were implemented to enhance the signal-to-noise ratio of spectra data. Various classification algorithms namely K-nearest neighbours (KNN), logistic regression (LR) and linear discriminant analysis (LDA) were implemented to classify the severity of dental caries into International Caries Detection and Assessment System (ICDAS) scores. The performance of the prediction model was measured and comparatively analysed based on the accuracy, precision, sensitivity, and specificity. The LDA algorithm combined with the Savitzky-Golay preprocessing method had shown the best result with respect to the validation data accuracy, precision, sensitivity and specificity, where each had values of 0.90, 1.00, 0.86 and 1.00 respectively. The area under the curve of the ROC plot computed for the LDA algorithm was 0.95, which indicated that the prediction algorithm was capable of differentiating normal and caries teeth excellently. © 2021 Elsevier B.V.
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