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Related Concept Videos

Diabetes Mellitus: Type 2 and Gestational01:22

Diabetes Mellitus: Type 2 and Gestational

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Type 2 diabetes, characterized by insulin resistance, arises when the insulin receptors on cells lose responsiveness to insulin, diminishing the cell's capacity to take up glucose, resulting in elevated blood glucose levels. To receive a diagnosis of Type 2 diabetes, a series of blood glucose tests are necessary to assess whether the blood glucose falls within normal parameters. If the result is out of the normal range, a patient may be diagnosed as prediabetic or diabetic, depending on the...
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For most patients, experiencing several weeks of polyuria, polydipsia, fatigue, and significant weight loss may indicate the presence of diabetes. Furthermore, adults displaying the phenotypic appearance of type 2 diabetes (particularly those who are obese and not initially insulin-requiring), may have islet cell autoantibodies, suggesting autoimmune-mediated β cell destruction and a diagnosis of latent autoimmune diabetes of adults (LADA). The categorization of glucose homeostasis is...
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Diabetes mellitus is a chronic metabolic disorder characterized by high blood glucose levels due to inadequate insulin production, insulin resistance, or both. The condition affects millions worldwide and can significantly impact their health and quality of life.
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Carbohydrates are polymers composed of molecules containing atoms of carbon, hydrogen and oxygen. One gram of carbohydrate can provide four kilo-calories of energy, which makes it the most efficient instant energy source.
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Related Experiment Video

Updated: Oct 12, 2025

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Noninvasive Prototype for Type 2 Diabetes Detection.

Javier Ferney Castillo García1, Jesús Hamilton Ortiz2, Osamah Ibrahim Khalaf3

  • 1Universidad Santiago de Cali, Facultad de Ingeniería, Cali, Colombia.

Journal of Healthcare Engineering
|November 19, 2021
PubMed
Summary

A new noninvasive device predicts type 2 diabetes using electrical bioimpedance and machine learning. This portable, low-cost tool achieves over 90% accuracy, enabling rapid, accessible diabetes screening.

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Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence in Healthcare
  • Preventive Medicine

Background:

  • Type 2 diabetes diagnosis relies on traditional methods, often requiring medical expertise and time.
  • Early screening is crucial for effective management and prevention of type 2 diabetes complications.

Purpose of the Study:

  • To develop and validate a human-safe, portable, noninvasive device for predicting type 2 diabetes.
  • To implement an artificial learning machine utilizing electrical bioimpedance and biometric data.
  • To create an accessible tool for early diabetes detection.

Main Methods:

  • Design and implementation of a portable, noninvasive device.
  • Utilizing electrical bioimpedance and biometric features for data acquisition.
  • Training an artificial learning machine with an active learning algorithm.
  • Development of an API with a graphical interface for data prediction and storage.

Main Results:

  • Achieved prediction accuracy exceeding 90% with statistical significance (p < 0.05).
  • Kappa coefficient values surpassed 0.9, indicating strong predictive capacity.
  • Demonstrated the device's effectiveness for type 2 diabetes screening.

Conclusions:

  • The developed device offers a reliable, noninvasive method for type 2 diabetes prediction.
  • The technology facilitates low-cost, comfortable, and rapid screening (< 2 minutes).
  • This innovation supports preventive medicine by enabling early identification of individuals at risk for type 2 diabetes.