Sánchez-Brito Miguel, Luna-Rosas Francisco J, Mendoza-González Ricardo, Mata-Miranda Mónica M, Martínez-Romo Julio C, Vázquez-Zapién Gustavo J
TecNM/Technological Institute of Aguascalientes, Aguascalientes, 20256, Mexico.
Military School of Medicine, Military Center for Health Sciences, Secretariat of National Defense, Mexico City, Mexico.
Talanta. 2021 Jan 1;221:121650. doi: 10.1016/j.talanta.2020.121650. Epub 2020 Sep 14.
The World Health Organization has declared that diabetes is one of the four leading causes of death attributable to non-communicable diseases. Currently, many devices allow monitoring blood glucose levels for diabetes control based mainly on blood tests. In this paper, we propose a novel methodology based on the analysis of the Fourier Transform Infrared (FTIR) spectra of saliva using machine learning techniques to characterize controlled and uncontrolled diabetic patients, clustering patients in groups of a low, medium, and high glucose levels, and finally performing the point estimation of a glucose value. After analyzing the obtained results with Support Vector Machines (SVM), Artificial Neural Networks (ANN), and Linear Regression (LR), we found that using ANN, it is possible to carry out the characterizations mentioned above efficiently since it allowed us to identify correctly the 540 spectra that make up our database studying the region 4000-2000 cm.
世界卫生组织已宣布，糖尿病是可归因于非传染性疾病的四大主要死因之一。目前，许多设备主要通过血液检测来监测血糖水平以控制糖尿病。在本文中，我们提出了一种新颖的方法，该方法基于使用机器学习技术分析唾液的傅里叶变换红外（FTIR）光谱，以表征血糖控制良好和控制不佳的糖尿病患者，将患者聚类为低、中、高血糖水平组，最后进行血糖值的点估计。在用支持向量机（SVM）、人工神经网络（ANN）和线性回归（LR）分析所得结果后，我们发现使用ANN可以有效地进行上述表征，因为它使我们能够正确识别构成我们数据库的540个光谱，研究的区域为4000 - 2000厘米。
