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

3.7K
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...
3.7K
Carbohydrate Metabolism01:36

Carbohydrate Metabolism

12.9K
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.
Starch accounts for approximately 60% of the carbohydrates consumed by humans. Since amylase enzymes cannot function in the stomach's acidic environment, starch can only be digested in the mouth and small intestine. Simple sugars are found naturally in milk and fruits in...
12.9K
Diabetes Mellitus: Overview and Type I Subtype01:22

Diabetes Mellitus: Overview and Type I Subtype

4.2K
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...
4.2K
Diabetes: Symptoms, Diagnosis, and Complications01:15

Diabetes: Symptoms, Diagnosis, and Complications

1.4K
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...
1.4K
Pathophysiology of Diabetes01:20

Pathophysiology of Diabetes

2.3K
Diabetes mellitus is a chronic metabolic disorder characterized by hyperglycemia. The four categories of diabetes are type 1 diabetes, type 2 diabetes, other specific types of diabetes, and gestational diabetes.
Type 1 diabetes is characterized by autoimmune-mediated destruction of pancreatic β cells, with environmental factors potentially triggering this process in genetically susceptible individuals. Despite many not having a family history, certain genes increase susceptibility,...
2.3K

You might also read

Related Articles

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

Sort by
Same author

A Computational Model for Determining Labeling Duration in Protein Turnover Studies Using a Single Deuterated Water Labeled Sample.

Journal of the American Society for Mass Spectrometry·2026
Same author

Proteostasis Modelling using Deuterated Water Metabolic Labeling and Data-Independent Acquisition Mass Spectrometry.

bioRxiv : the preprint server for biology·2025
Same author

Duplexing metabolic deuterated water-labeled samples using dimethyl labeling to estimate protein turnover rates.

Communications chemistry·2025
Same author

Turnover Rates and Numbers of Exchangeable Hydrogens in Deuterated Water Labeled Samples.

International journal of molecular sciences·2025
Same author

Numbers of Exchangeable Hydrogens from LC-MS Data of Heavy Water Metabolically Labeled Samples.

Journal of the American Society for Mass Spectrometry·2024
Same author

Exact Integral Formulas for False Discovery Rate and the Variance of False Discovery Proportion.

Journal of proteome research·2024

Related Experiment Video

Updated: Nov 10, 2025

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

7.1K

Prediction of Type 2 Diabetes Based on Machine Learning Algorithm.

Henock M Deberneh1, Intaek Kim1

  • 1Department of Information and Communications Engineering, Myongji University, 116 Myongji-ro, Yongin, Gyeonggi 17058, Korea.

International Journal of Environmental Research and Public Health
|April 3, 2021
PubMed
Summary
This summary is machine-generated.

This study developed a machine learning model to predict type 2 diabetes (T2D) risk using electronic health records. Ensemble models showed superior performance in forecasting T2D occurrence in the Korean population.

Keywords:
machine learningpredictiontype 2 diabetes

More Related Videos

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.5K
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

7.7K

Related Experiment Videos

Last Updated: Nov 10, 2025

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

7.1K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.5K
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

7.7K

Area of Science:

  • Medical Informatics
  • Computational Biology
  • Public Health

Background:

  • Early prediction of type 2 diabetes (T2D) enables preventative interventions.
  • Machine learning (ML) offers potential for accurate T2D risk assessment.

Purpose of the Study:

  • To develop and evaluate an ML model for predicting T2D occurrence within one year.
  • To identify key clinical and lifestyle variables for T2D prediction.

Main Methods:

  • Utilized electronic health records (2013-2018) from a Korean medical institute.
  • Selected features using statistical tests (ANOVA, chi-squared) and recursive feature elimination.
  • Trained and compared logistic regression, random forest, SVM, XGBoost, and ensemble ML algorithms.

Main Results:

  • Ensemble ML models demonstrated superior predictive performance compared to single models.
  • Key predictors included fasting plasma glucose (FPG), HbA1c, BMI, and lifestyle factors.
  • Cross-validation confirmed the effectiveness of the prediction models.

Conclusions:

  • The developed ML model provides valuable predictive insights for T2D risk.
  • Incorporating extensive medical history enhances prediction model performance.
  • The model supports clinicians and patients in managing T2D risk.