Noninvasive Prototype for Type 2 Diabetes Detection

Citations Over TimeTop 10% of 2021 papers

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.

Related Papers

• → Introduction to Biometrics(2006)109 cited
• → Soft biometrics—combining body weight and fat measurements with fingerprint biometrics(2005)85 cited
• → A Various Biometric application for authentication and identification(2019)1 cited
• → Well proven and commercially available biometric technologies versus electrocardiogram: A review(2014)
• Progress in Biometric Computing(2011)