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Related Concept Videos

Diabetes Mellitus: Introduction01:26

Diabetes Mellitus: Introduction

24
Diabetes mellitus consists of chronic metabolic disorders characterized by persistent hyperglycemia. This elevated blood glucose results from defects in insulin secretion, impaired insulin action, or both. Insulin, produced by pancreatic β-cells, is essential for maintaining glucose homeostasis by facilitating cellular glucose uptake for energy or storage. Disruptions in insulin production or function lead to glucose accumulation in the bloodstream, causing the clinical features and...
24
Type II Diabetes Mellitus III: Clinical Manifestations and Diagnosis01:25

Type II Diabetes Mellitus III: Clinical Manifestations and Diagnosis

17
Type 2 diabetes mellitus develops gradually and is often asymptomatic in early stages.Clinical ManifestationsWhen symptoms appear, they include fatigue, blurred vision, pruritus, delayed wound healing, and recurrent infections, particularly candidal infections. Peripheral neuropathy may present as numbness or tingling in the extremities. Classic hyperglycemia symptoms—polyuria, polydipsia, and polyphagia—are less common. Most patients are overweight and frequently have associated...
17
Diabetes Mellitus: Overview and Type I Subtype01:22

Diabetes Mellitus: Overview and Type I Subtype

5.5K
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...
5.5K
Diabetes Mellitus: Type 2 and Gestational01:22

Diabetes Mellitus: Type 2 and Gestational

4.9K
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...
4.9K
Diabetic Retinopathy01:27

Diabetic Retinopathy

55
DefinitionDiabetic retinopathy is a microvascular complication of diabetes affecting the retinal blood vessels.Risk FactorsDiabetic retinopathy is present in almost all individuals with type 1 diabetes and more than 60% of those with type 2 diabetes after two decades of disease.The risk increases with poor glycemic control, hypertension, dyslipidemia, smoking, pregnancy, and puberty.Although cataracts and glaucoma are also more frequent in people with diabetes, retinopathy remains the leading...
55
Type II Diabetes I: Introduction01:26

Type II Diabetes I: Introduction

17
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...
17

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Related Experiment Video

Updated: May 5, 2026

Modeling and Evaluation of Murine Diabetic Cardiomyopathy Model
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Modeling and Evaluation of Murine Diabetic Cardiomyopathy Model

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An Effective Model-Based Voting Classifier for Diabetes Mellitus Classification.

Diyar Qader Zeebaree1, Merdin Shamal Salih2, Danial William Odeesho3

  • 1Department of Cyber Security Techniques Engineering, College of Computer and AI, Northern Technical University, Mosul 41001, Iraq.

Bioengineering (Basel, Switzerland)
|May 4, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a novel hybrid machine learning framework for early diabetes detection. The RFE + CFS + SVM model achieved 98.0% accuracy, offering a robust tool for timely clinical decisions.

Keywords:
PIAM and Frankfurtclassificationdiabetic predictionfeature selection

Related Experiment Videos

Last Updated: May 5, 2026

Modeling and Evaluation of Murine Diabetic Cardiomyopathy Model
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Area of Science:

  • Medical Informatics
  • Computational Biology
  • Machine Learning in Healthcare

Background:

  • Diabetes mellitus is a rapidly growing global health concern affecting over 347 million people.
  • Early detection of diabetes is crucial for preventing complications and improving patient outcomes.
  • Existing machine learning models for diabetes classification often lack robust feature selection and ensemble methods, limiting their predictive accuracy and generalizability.

Purpose of the Study:

  • To develop and evaluate a hybrid machine learning framework integrating advanced feature selection and ensemble techniques for enhanced diabetes prediction.
  • To improve the accuracy, robustness, and generalizability of early-stage diabetes detection models.

Main Methods:

  • A hybrid framework combining three feature selection algorithms (Genetic Algorithm, Correlation-Based Feature Selection, Recursive Feature Elimination) in single and hybrid forms.
  • Integration of three classifiers (Multi-Layer Perceptron, Support Vector Machine, Random Forest) using soft voting for ensemble prediction.
  • Evaluation on a benchmark diabetes dataset to assess performance metrics including accuracy, sensitivity, specificity, precision, and F1-score.

Main Results:

  • The Recursive Feature Elimination + Correlation-Based Feature Selection + Support Vector Machine (RFE + CFS + SVM) combination demonstrated superior performance.
  • Achieved an accuracy of 98.0%, sensitivity of 97.43%, specificity of 99.03%, precision of 99.51%, and F1-score of 98.72%.
  • The hybrid approach significantly enhanced predictive robustness compared to existing methods.

Conclusions:

  • The proposed hybrid feature-selection and ensemble learning model provides a highly effective and robust approach for early diabetes diagnosis.
  • This model can empower clinicians with timely and accurate decision-making capabilities for diabetes management.
  • The findings highlight the potential of advanced machine learning techniques in addressing critical public health challenges like diabetes.