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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 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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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.
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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.
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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.
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Related Experiment Video

Updated: Oct 22, 2025

Behavioral Assessment of Visual Function via Optomotor Response and Cognitive Function via Y-Maze in Diabetic Rats
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Diabetes Mellitus Prediction and Severity Level Estimation Using OWDANN Algorithm.

Annamalai R1, Nedunchelian R2

  • 1Department of Information Technology, Jeppiaar Institute of Technology, Kanchipuram 631604, India.

Computational Intelligence and Neuroscience
|August 31, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel algorithm for accurate diabetes mellitus prediction and severity estimation. The proposed method achieves high accuracy, improving early diagnosis and reducing disease risk.

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Area of Science:

  • Biomedical Informatics
  • Machine Learning in Healthcare
  • Computational Biology

Background:

  • Diabetes mellitus is a prevalent chronic disease with serious complications.
  • Accurate early diagnosis is crucial for reducing diabetes risk and incidence.
  • Existing prediction methods often suffer from low accuracy and high training times.

Purpose of the Study:

  • To propose an Optimized Whale Decomposition Artificial Neural Network (OWDANN) algorithm for diabetes mellitus prediction.
  • To develop a system for estimating the severity level of diabetes.
  • To address the limitations of existing methods in terms of accuracy and training efficiency.

Main Methods:

  • The proposed system involves two phases: disease prediction and severity level estimation.
  • For prediction, the Pima dataset undergoes preprocessing, feature extraction, and classification using OWDANN.
  • For severity estimation, a diabetes-positive dataset is preprocessed, features are extracted, and severity is predicted using GDHC.

Main Results:

  • The proposed system achieved high performance metrics: 98.97% accuracy, 94.98% sensitivity, 95.62% specificity, 97.02% precision, 93.84% recall, and 94.04% F-measure.
  • The system demonstrated low False Discovery Rate (FDR) at 0.094% and False Positive Rate (FPR) at 0.023%.
  • The OWDANN algorithm significantly outperformed existing state-of-the-art methods in diabetes prediction and severity estimation.

Conclusions:

  • The developed OWDANN algorithm offers a highly accurate and efficient solution for diabetes mellitus prediction.
  • The system's ability to estimate severity levels provides valuable clinical insights.
  • This approach holds significant potential for improving early detection and management of diabetes.