Noninvasive Prototype for Type 2 Diabetes Detection

dc.contributor.authorCastillo García, Javier Ferney
dc.contributor.authorOrtiz, Jesús Hamilton
dc.contributor.authorIbrahim Khalaf, Osamah
dc.contributor.authorValencia Hernández, Adrián David
dc.contributor.authorRodríguez Timaná, Luis Carlos
dc.date.accessioned2025-07-17T19:43:43Z
dc.date.available2025-07-17T19:43:43Z
dc.date.issued2021
dc.description.abstractThe present work demonstrates the design and implementation of a human-safe, portable, noninvasive device capable of predicting type 2 diabetes, using electrical bioimpedance and biometric features to train an artificial learning machine using an active learning algorithm based on population selection. In addition, there is an API with a graphical interface that allows the prediction and storage of data when the characteristics of the person are sent. The results obtained show an accuracy higher than 90% with statistical significance (p < 0.05). The Kappa coefficient values were higher than 0.9, showing that the device has a good predictive capacity which would allow the screening process of type 2 diabetes. This development contributes to preventive medicine and makes it possible to determine at a low cost, comfortably, without medical preparation, and in less than 2 minutes whether a person has type 2 diabetes.
dc.identifier.citationCastillo García, J. F., Ortiz, J. H., Ibrahim Khalaf, O., Valencia Hernández, A. D., & Rodríguez Timaná, L. C. (2021). Noninvasive Prototype for Type 2 Diabetes Detection. Journal of Healthcare Engineering, 2021. https://doi.org/10.1155/2021/8077665 Ramírez-Castrillón, M., Jaramillo-Garcia, V. P., Lopes Barros, H., Pegas Henriques, J. A., Stefani, V., & Valente, P. (2021). Nile Red Incubation Time Before Reading Fluorescence Greatly Influences the Yeast Neutral Lipids Quantification. Frontiers in Microbiology, 12. https://doi.org/10.3389/FMICB.2021.619313
dc.identifier.issn20402295
dc.identifier.urihttps://repositorio.usc.edu.co/handle/20.500.12421/7492
dc.language.isoen
dc.subjectAlgorithms
dc.subjectDiabetes Mellitus
dc.subjectType 2
dc.subjectHumans
dc.subjectMachine Learning
dc.titleNoninvasive Prototype for Type 2 Diabetes Detection
dc.typeArticle

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