abstract

• This work describes the application of portable Raman spectroscopy coupled with Artificial Neural Networks (ANN), to discern between diabetic patients and healthy controls, with a high degree of accuracy (Acc=89.7±6.6%25). This technique is relatively low-cost, simple and comfortable for the patient, yielding rapid diagnosis. These features make our method a promising screening tool for identifying type 2 diabetes mellitus (DM2) in a non-invasive and automated fashion. © 2017 IEEE.

authors

• González Contreras Francisco Javier
• Guevara Codina Edgar
• Luevano-Contreras C.
• Ramírez Elías Miguel Ghebre
• Torres-Galván J.C.

publication date

• 2017-01-01

published in

• 2017 Photonics North, PN 2017  Proceedings

keywords

• diabetes; machine learning; Raman spectroscopy Learning systems; Medical problems; Neural networks; Raman spectroscopy; Diabetic patient; Healthy controls; High degree of accuracy; In-vivo; Low costs; Portable Raman spectroscopy; Screening tool; Type 2 diabetes mellitus; Diagnosis

Digital Object Identifier (DOI)

• 10.1109/PN.2017.8090556

start page

end page