PCA-KNN for Detection of NS1 from SERS Salivary Spectra

Othman, N. H.; Lee, Khuan Y.; Radzol, A. R. M.; Mansor, W.; Wong, Patrick; Looi, Irene

doi:10.1007/978-3-319-75420-8_32

Cited by 3 publications

(1 citation statement)

References 25 publications

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“…Huang et al 27 compared the prediction accuracy of quadratic discriminant analysis (QDA), 91 K-nearst neighbor (KNN), 92 ANN, XGBoost (XGB), 93 and SVM models. Raman spectra of the plasma-derived extracellular vesicles of patients with various degrees of CAD were used to train and predict CAD: stable nonobstructive plaques (SP), non-ST-elevation myocardial infarction, ST-elevation myocardial infarction CAD, and healthy controls without CAD.…”

Section: Artificial Intelligence-based Detectionmentioning

confidence: 99%

Recent Trends in Surface-Enhanced Raman Spectroscopy-Based Biosensors: Label-Free Early Disease Diagnosis

Jin,

Lednev,

Jung

2024

J. Phys. Chem. C

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The recent advances in surface-enhanced Raman spectroscopy (SERS) have developed label-free biosensors for early diagnosis of diseases. This review explores the recent trends of SERS-based biosensors, focusing on their potential applications for the rapid and accurate detection of diseases without labeling agents. The integration of SERS with biosensor platforms provides a powerful approach to enhance sensitivity and selectivity in the detection of biomolecules associated with diseases. The integration of artificial intelligence to optimize data analysis and improve diagnostic accuracy is discussed. This study provides valuable insight into the current landscape of SERS-based biosensors for label-free early diagnosis of diseases by summarizing key studies and innovations.

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