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: Overview and Type I Subtype01:22

Diabetes Mellitus: Overview and Type I Subtype

2.4K
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...
2.4K
Diabetes Mellitus: Type 2 and Gestational01:22

Diabetes Mellitus: Type 2 and Gestational

2.2K
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...
2.2K
Pathophysiology of Diabetes01:20

Pathophysiology of Diabetes

862
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,...
862
Diabetes: Symptoms, Diagnosis, and Complications01:15

Diabetes: Symptoms, Diagnosis, and Complications

503
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...
503
Carbohydrate Metabolism01:36

Carbohydrate Metabolism

10.8K
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...
10.8K
Diabetes: Management and Pharmacotherapy01:15

Diabetes: Management and Pharmacotherapy

234
The therapy for diabetes aims to alleviate hyperglycemia-related symptoms, prevent acute metabolic decompensation, and reduce chronic end-organ complications. Glycemic control is evaluated through short-term (self-monitoring, continuous glucose monitoring) and long-term (A1c, fructosamine) metrics, enabling near real-time tracking of blood glucose levels and reflecting glycemic control over specific time frames.
Insulin remains the cornerstone of treatment for most patients with type 1 and many...
234

You might also read

Related Articles

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

Sort by
Same author

Pathways and Roadblocks: Navigating Family-Building for Sexual and Gender Minority People Assigned Male at Birth.

Perspectives on sexual and reproductive health·2026
Same author

Oral creatine in hemodialysis patients increases physical functional capacity and muscle mass, an open label study.

PloS one·2025
Same author

Genomic Characterization of Extremely Antibiotic-Resistant Strains of <i>Pseudomonas aeruginosa</i> Isolated from Patients of a Clinic in Sincelejo, Colombia.

Biotech (Basel (Switzerland))·2025
Same author

Whole-Genome Sequencing of Resistance, Virulence and Regulation Genes in Extremely Resistant Strains of <i>Pseudomonas aeruginosa</i>.

Medical sciences (Basel, Switzerland)·2025
Same author

Our needs have been ignored for a long time: Factors affecting willingness of Black and Hispanic/Latinx sexual and gender minority communities to donate biospecimens.

Annals of LGBTQ public and population health·2024
Same author

Heart Failure with Reduced Ejection Fraction and COVID-19, when the Sick Get Sicker: Unmasking Racial and Ethnic Inequities During a Pandemic.

Heart failure clinics·2024

Related Experiment Video

Updated: Jun 4, 2025

An In Ovo Model for Testing Insulin-mimetic Compounds
06:09

An In Ovo Model for Testing Insulin-mimetic Compounds

Published on: April 23, 2018

10.5K

An explainable analysis of diabetes mellitus using statistical and artificial intelligence techniques.

William Hoyos1,2,3, Kenia Hoyos4, Rander Ruiz5

  • 1Grupo de Investigación ISI, Universidad Cooperativa de Colombia, Montería, Colombia. william.hoyos@campusucc.edu.co.

BMC Medical Informatics and Decision Making
|December 19, 2024
PubMed
Summary

This study developed an explainable analysis for diabetes mellitus (DM) using AI and statistical methods. The XGBoost model achieved perfect accuracy, while fuzzy cognitive maps offered valuable insights into risk factors.

Keywords:
Artificial intelligenceDiabetes mellitusExplainabilityPredictive modelsStatistics

More Related Videos

Leprdb Mouse Model of Type 2 Diabetes: Pancreatic Islet Isolation and Live-cell 2-Photon Imaging Of Intact Islets
10:09

Leprdb Mouse Model of Type 2 Diabetes: Pancreatic Islet Isolation and Live-cell 2-Photon Imaging Of Intact Islets

Published on: May 11, 2015

9.4K
Computer-assisted Large-scale Visualization and Quantification of Pancreatic Islet Mass, Size Distribution and Architecture
16:59

Computer-assisted Large-scale Visualization and Quantification of Pancreatic Islet Mass, Size Distribution and Architecture

Published on: March 4, 2011

12.2K

Related Experiment Videos

Last Updated: Jun 4, 2025

An In Ovo Model for Testing Insulin-mimetic Compounds
06:09

An In Ovo Model for Testing Insulin-mimetic Compounds

Published on: April 23, 2018

10.5K
Leprdb Mouse Model of Type 2 Diabetes: Pancreatic Islet Isolation and Live-cell 2-Photon Imaging Of Intact Islets
10:09

Leprdb Mouse Model of Type 2 Diabetes: Pancreatic Islet Isolation and Live-cell 2-Photon Imaging Of Intact Islets

Published on: May 11, 2015

9.4K
Computer-assisted Large-scale Visualization and Quantification of Pancreatic Islet Mass, Size Distribution and Architecture
16:59

Computer-assisted Large-scale Visualization and Quantification of Pancreatic Islet Mass, Size Distribution and Architecture

Published on: March 4, 2011

12.2K

Area of Science:

  • Computational biology
  • Medical informatics
  • Data science

Background:

  • Diabetes mellitus (DM) is a global chronic disease requiring advanced analytical methods for early detection and management.
  • Sociodemographic and clinical data are crucial for understanding and predicting DM.
  • Multifaceted approaches are needed to improve patient outcomes and reduce mortality rates.

Purpose of the Study:

  • To develop an explainable analysis of DM by integrating sociodemographic and clinical data.
  • To compare the predictive performance of various statistical and artificial intelligence (AI) techniques for DM classification.
  • To assess the risk factors associated with DM using explainable AI methods.

Main Methods:

  • Utilized a dataset comprising sociodemographic and clinical profiles of diabetic and non-diabetic individuals.
  • Applied statistical tests including Student's t-test and Chi-square.
  • Employed AI techniques such as fuzzy cognitive maps (FCM), artificial neural networks (ANN), support vector machines (SVM), and XGBoost.

Main Results:

  • Statistical models identified significant variable associations.
  • AI models demonstrated high efficacy in DM classification, with XGBoost achieving perfect accuracy, sensitivity, and specificity.
  • FCM provided explainability through scenario-based simulations, highlighting key predictive variables.

Conclusions:

  • An integrated analytical approach combining diverse methodologies is essential for timely DM detection.
  • Informed clinical decision-making can be enhanced through explainable AI and statistical analysis.
  • This research underscores the importance of combining predictive power with interpretability in disease analysis.