In Journal of biophotonics
This letter aims to reply to Bratchenko and Bratchenko's comment on our paper "Feasibility of Raman spectroscopy as a potential in vivo tool to screen for pre-diabetes and diabetes". Our paper analyzed the feasibility of using in vivo Raman measurements combined with machine learning techniques to screen diabetic and pre-diabetic patients. We argued that this approach yields high overall accuracy (94.3%) while retaining a good capacity to distinguish between diabetic (AUC=0.86) and control classes (AUC=0.97) and a moderate performance for the pre-diabetic class (AUC=0.76). Bratchenko and Bratchenko's comment focuses on the possible overestimation of the proposed classification models and the absence of information on the age of participants. In this reply, we address their main concerns regarding our previous manuscript. This article is protected by copyright. All rights reserved.
Guevara Edgar, Torres-Galván Juan Carlos, González Francisco Javier, Luevano-Contreras Claudia, Castillo-Martínez Claudio Cayetano, Ramírez-Elías Miguel G
2022-Oct-28
Raman spectroscopy, diabetes, machine learning, principal component analysis, support vector machine
