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Open Access 05-09-2022 | Prediabetes | Original Article

Quantification of glycated hemoglobin and glucose in vivo using Raman spectroscopy and artificial neural networks

Authors: Naara González-Viveros, Jorge Castro-Ramos, Pilar Gómez-Gil, Hector Humberto Cerecedo-Núñez, Francisco Gutiérrez-Delgado, Enrique Torres-Rasgado, Ricardo Pérez-Fuentes, Jose L. Flores-Guerrero

Published in: Lasers in Medical Science | Issue 9/2022

Abstract

Undiagnosed type 2 diabetes (T2D) remains a major public health concern. The global estimation of undiagnosed diabetes is about 46%, being this situation more critical in developing countries. Therefore, we proposed a non-invasive method to quantify glycated hemoglobin (HbA1c) and glucose in vivo. We developed a technique based on Raman spectroscopy, RReliefF as a feature selection method, and regression based on feed-forward artificial neural networks (FFNN). The spectra were obtained from the forearm, wrist, and index finger of 46 individuals. The use of FFNN allowed us to achieve an error in the predictive model of 0.69% for HbA1c and 30.12 mg/dL for glucose. Patients were classified according to HbA1c values into three categories: healthy, prediabetes, and T2D. The proposed method obtained a specificity and sensitivity of 87.50% and 80.77%, respectively. This work demonstrates the benefit of using artificial neural networks and feature selection techniques to enhance Raman spectra processing to determine glycated hemoglobin and glucose in patients with undiagnosed T2D.

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Title: Quantification of glycated hemoglobin and glucose in vivo using Raman spectroscopy and artificial neural networks
Authors: Naara González-Viveros
Jorge Castro-Ramos
Pilar Gómez-Gil
Hector Humberto Cerecedo-Núñez
Francisco Gutiérrez-Delgado
Enrique Torres-Rasgado
Ricardo Pérez-Fuentes
Jose L. Flores-Guerrero
Publication date: 05-09-2022
Publisher: Springer London
Keywords: Prediabetes
Type 2 Diabetes
Published in: Lasers in Medical Science / Issue 9/2022
Print ISSN: 0268-8921
Electronic ISSN: 1435-604X
DOI: https://doi.org/10.1007/s10103-022-03633-w

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Quantification of glycated hemoglobin and glucose in vivo using Raman spectroscopy and artificial neural networks

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