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Diabetes: Symptoms, Diagnosis, and Complications01:15

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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...
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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,...
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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.
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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...
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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...
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Related Experiment Video

Updated: May 24, 2025

Deep Neural Networks for Image-Based Dietary Assessment
13:19

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Published on: March 13, 2021

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New AI explained and validated deep learning approaches to accurately predict diabetes.

Ifra Shaheen1, Nadeem Javaid2, Nabil Alrajeh3

  • 1ComSens Lab, International Graduate School of Artificial Intelligence, National Yunlin University of Science and Technology, Douliou, Yunlin, 64002, Taiwan.

Medical & Biological Engineering & Computing
|March 4, 2025
PubMed
Summary
This summary is machine-generated.

Two novel deep learning models, LeDNet and HiDenNet, significantly improve early diabetes prediction accuracy. They address class imbalance and enhance model interpretability, outperforming existing methods for reliable clinical decision-making.

Keywords:
Deep learningDense networkDiabetes predictionDual attention networkExplainable artificial intelligenceMajority weighted minority over-sampling technique

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Area of Science:

  • * Computational biology and machine learning applications in healthcare.
  • * Development of advanced artificial intelligence (AI) algorithms for disease prediction.

Background:

  • * Diabetes mellitus is a critical metabolic condition requiring early detection to prevent severe chronic complications and organ failure.
  • * Existing predictive models for diabetes often suffer from low accuracy, class imbalance issues, and a lack of transparency in their decision-making processes.
  • * Deep learning (DL) models show potential but require enhancements for practical clinical application.

Purpose of the Study:

  • * To introduce two novel deep learning models, LeDNet and HiDenNet, for enhanced early and accurate diabetes prediction.
  • * To address the challenges of class imbalance and poor interpretability in current diabetes prediction models.
  • * To improve the reliability and transparency of AI-driven tools for clinical decision support in diabetes diagnosis.

Main Methods:

  • * Development and training of two novel deep learning architectures: LeDNet (LeNet + Dual Attention Network) and HiDenNet (Highway Network + DenseNet).
  • * Utilization of the Diabetes Health Indicators dataset, with mitigation of class imbalance via majority-weighted minority over-sampling.
  • * Application of K-fold cross-validation for model stability assessment and integration of explainable AI (XAI) techniques (LIME, SHAP) for interpretability.

Main Results:

  • * LeDNet achieved an F1-score of 85%, recall of 84%, accuracy of 85%, and precision of 86%.
  • * HiDenNet demonstrated comparable performance with accuracy, F1-score, recall, and precision all at 85% or 86%.
  • * Both models outperformed existing state-of-the-art deep learning models and provided interpretable feature insights through XAI.

Conclusions:

  • * LeDNet and HiDenNet offer significant improvements in accuracy and reliability for early diabetes prediction compared to current DL models.
  • * The proposed models effectively handle class imbalance and provide crucial interpretability, addressing key limitations of previous approaches.
  • * These explainable AI-enhanced models represent promising tools for clinical decision-making and early diabetes diagnosis, enhancing transparency and trust.