Noninvasive Prototype for Type 2 Diabetes Detection
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Date
2021
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Abstract
The 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.
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Keywords
Algorithms, Diabetes Mellitus, Type 2, Humans, Machine Learning
Citation
Castillo 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