Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Diabetes Mellitus: Type 2 and Gestational01:22

Diabetes Mellitus: Type 2 and Gestational

5.7K
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...
5.7K
Diabetes: Symptoms, Diagnosis, and Complications01:15

Diabetes: Symptoms, Diagnosis, and Complications

2.9K
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...
2.9K
Diabetes Mellitus: Overview and Type I Subtype01:22

Diabetes Mellitus: Overview and Type I Subtype

6.2K
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.
Type 1 diabetes is an autoimmune disease in which the immune system mistakenly attacks and destroys the insulin-producing beta cells in the pancreas. As a result, the body is unable to produce sufficient insulin, and individuals with...
6.2K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

MERWACS: Development and external validation of a non-invasive machine learning tool for identifying subjects to be screened for CKD.

PLOS digital health·2026
Same author

Not all antibodies are created equal: total IgG glycosylation and severity of antibody-mediated rejection in kidney transplantation.

Transplant international : official journal of the European Society for Organ Transplantation·2026
Same author

The effects of presenting health and environmental impacts of food on consumption intentions.

Food quality and preference·2026
Same author

When Chronic Kidney Disease Therapies Meet the Allograft: Lessons From Dapagliflozin.

Transplantation·2026
Same author

The Impact of Initial Kidney Graft Function on Long-term Outcomes: A Matter of Definition?

Transplantation·2026
Same author

Multidrug-Resistant Gram-Negative Urinary Tract Infections (UTIs) Increase the Risk of Rejection, CMV Viremia, and Graft Loss After Kidney Transplantation.

Transplant infectious disease : an official journal of the Transplantation Society·2026

Related Experiment Video

Updated: Apr 10, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

8.2K

Enhancing prediabetes and diabetes detection through a machine learning-enabled self-assessment approach.

Daniel Yoo1, Umberto Maggiore2, Olivier Jolliet3

  • 1Section for Quantitative Sustainability Assessment, Department of Environmental and Resource Engineering, Technical University of Denmark, Kgs Lyngby 2800, Denmark.

Journal of Clinical Epidemiology
|April 8, 2026
PubMed
Summary
This summary is machine-generated.

A new machine learning system, MEDWACS, uses 7 accessible health parameters for non-invasive prediabetes/diabetes screening. This tool aids early detection and intervention, potentially reducing public health burdens.

Keywords:
DiabetesEarly warning systemMachine learningNHANESPrediabetesPrevention

More Related Videos

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.7K

Related Experiment Videos

Last Updated: Apr 10, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

8.2K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.7K

Area of Science:

  • Machine learning applications in public health
  • Diabetes and prediabetes risk prediction
  • Non-invasive health screening technologies

Background:

  • Lack of reliable, accessible, non-invasive self-assessment screening for prediabetes/diabetes hinders early intervention.
  • Machine learning (ML) offers potential for developing novel risk prediction tools.

Purpose of the Study:

  • To develop and externally validate an ML-derived self-assessment system (MEDWACS) for predicting prediabetes/diabetes likelihood.
  • To identify easily accessible health parameters for self-assessment screening.

Main Methods:

  • Analysis of 30 years of NHANES data (N=17,458) using multimodal data for ML model development.
  • Identification of key predictors using the Boruta algorithm and selection of 7 accessible parameters for the MEDWACS model.
  • External validation using NHANES 2021-2023 and Korea NHANES 2023 data.

Main Results:

  • The 7-parameter MEDWACS model includes age, waist circumference, systolic blood pressure, gender, upper leg length, arm circumference, and BMI.
  • Achieved strong internal (ROCAUC 0.804) and external validation performance (US ROCAUC 0.773, Korea ROCAUC 0.780).
  • Demonstrated superior clinical utility compared to established screening guidelines, with an online tool developed for accessibility.

Conclusions:

  • MEDWACS is a validated, non-invasive ML tool for risk stratification of prediabetes/diabetes using 7 accessible parameters.
  • Facilitates timely clinical evaluations, potentially mitigating the public health impact of diabetes.
  • Enables home-based self-assessment and supports clinical decision-making for early intervention.