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Related Concept Videos

Diabetes Mellitus: Type 2 and Gestational01:22

Diabetes Mellitus: Type 2 and Gestational

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...
Type II Diabetes I: Introduction01:26

Type II Diabetes I: Introduction

Type 2 diabetes mellitus (T2DM) is a chronic metabolic disorder characterized by insulin resistance, in which target tissues such as the liver, muscle, and adipose tissue respond poorly to insulin. It is also associated with inadequate compensatory insulin secretion, where pancreatic β-cells fail to produce sufficient insulin. Together, these abnormalities lead to persistent hyperglycemia.EtiologyT2DM develops through a complex interaction of genetic predisposition and environmental or...
Type II Diabetes II: Pathophysiology01:24

Type II Diabetes II: Pathophysiology

PathophysiologyType 2 diabetes mellitus (T2DM ) is a chronic metabolic disorder characterized by insulin resistance and progressive pancreatic β-cell dysfunction, leading to impaired glucose homeostasis. It results from interactions among genetic predisposition, environmental factors, and metabolic stressors, such as overnutrition and a sedentary lifestyle.Insulin Resistance and Glucose DysregulationEarly T2DM involves insulin resistance in skeletal muscle, adipose tissue, and the liver.
Type I Diabetes II: Pathophysiology01:26

Type I Diabetes II: Pathophysiology

Type 1 diabetes mellitus arises from an immune-mediated destruction of pancreatic β-cells, resulting in an absolute deficiency of insulin. This process develops in genetically susceptible individuals when autoimmunity, environmental exposures, and immunologic dysregulation converge to trigger a targeted attack on the insulin-producing cells of the pancreas. The β-cells are located within the islets of Langerhans and are essential for regulating blood glucose by facilitating cellular uptake of...
Diabetes Mellitus: Overview and Type I Subtype01:22

Diabetes Mellitus: Overview and Type I Subtype

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...
Type I Diabetes I: Introduction01:12

Type I Diabetes I: Introduction

Type 1 diabetes mellitus is a chronic metabolic disorder characterized by an absolute deficiency of insulin resulting from the autoimmune destruction of pancreatic β-cells. Although it can occur at any age, it is most commonly diagnosed in childhood, adolescence, or early adulthood. The loss of insulin production impairs cellular glucose uptake, resulting in persistent hyperglycemia and necessitating lifelong insulin therapy.Autoimmune Destruction of β-CellsThe hallmark of type 1 diabetes is an...

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Related Experiment Video

Updated: Jul 12, 2026

Modeling and Evaluation of Murine Diabetic Cardiomyopathy Model
06:22

Modeling and Evaluation of Murine Diabetic Cardiomyopathy Model

Published on: November 29, 2024

Generative AI models for type 2 diabetes mellitus risk prediction.

Elsa Sharu Johnson1, Uma Gandhi1, U Srinivasulu Reddy2

  • 1The Department of Instrumentation and Control Engineering, National Institute of Technology, Tiruchirappalli, India.

Health Systems (Basingstoke, England)
|July 11, 2026
PubMed
Summary

This study uses Generative Artificial Intelligence (GenAI) and advanced feature selection to accurately predict Type II Diabetes Mellitus (T2DM) risk, improving upon existing methods for better patient outcomes.

Keywords:
Type II diabetes mellitusfeature selectiongenerative AIlarge language modelsmachine learning and deep learningonset and risk prediction

Related Experiment Videos

Last Updated: Jul 12, 2026

Modeling and Evaluation of Murine Diabetic Cardiomyopathy Model
06:22

Modeling and Evaluation of Murine Diabetic Cardiomyopathy Model

Published on: November 29, 2024

Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Data Science

Background:

  • Type II Diabetes Mellitus (T2DM) is a widespread chronic condition with significant global health implications.
  • Accurate prediction of T2DM onset and risk is crucial for timely intervention and management.
  • Challenges in T2DM prediction include data scarcity and class imbalance, hindering model performance.

Purpose of the Study:

  • To enhance the accuracy of T2DM prediction using Generative Artificial Intelligence (GenAI) for synthetic data generation.
  • To implement innovative feature selection methods for identifying optimal predictors of T2DM.
  • To evaluate the performance of various Machine Learning (ML), Ensemble Learning (EL), and Deep Learning (DL) models on T2DM prediction tasks.

Main Methods:

  • Utilized GenAI models, including Deep Tabular Augmentation (DTA) and Large Language Models (LLM), to generate synthetic data, addressing class imbalance and data scarcity.
  • Employed the Representative Instances-based Fuzzy Rough Set Feature Selection (FRS-RI) method for effective feature selection.
  • Trained and evaluated ML, EL, and DL models on diverse diabetes datasets (Sylhet, Obesity, Diagnostic Features) and benchmark datasets (PIMA, LMCH).

Main Results:

  • Achieved high accuracy, precision, and recall scores across multiple datasets, with specific models reaching 100% accuracy on the Obesity dataset.
  • Demonstrated strong generalization capabilities with enhanced test accuracies of 98.37% (PIMA) and 97.33% (LMCH) after applying the proposed techniques.
  • Highlighted the effectiveness of GenAI-driven synthetic data and FRS-RI feature selection in improving T2DM prediction models.

Conclusions:

  • The integration of GenAI for synthetic data generation and advanced feature selection significantly improves T2DM prediction accuracy.
  • The developed methodology offers a robust approach for identifying individuals at risk of T2DM, with potential for clinical application.
  • Emphasis on model explainability is vital for clinical trust and the justification of T2DM risk predictions.