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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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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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Diabetes Mellitus: Overview and Type I Subtype01:22

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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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Diabetes: Management and Pharmacotherapy01:15

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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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Updated: Oct 9, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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A Machine Learning Approach to Predicting Diabetes Complications.

Yazan Jian1, Michel Pasquier1, Assim Sagahyroon1

  • 1Department of Computer Science and Engineering, American University of Sharjah, Sharjah 26666, United Arab Emirates.

Healthcare (Basel, Switzerland)
|December 24, 2021
PubMed
Summary
This summary is machine-generated.

This study effectively predicted eight diabetes complications using machine learning models, achieving high accuracy. Feature selection demonstrated that fewer features can still yield adequate predictive classifiers for diabetes management.

Keywords:
diabetes complicationsdiabetes predictionsupervised learning

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Area of Science:

  • Medical Informatics
  • Machine Learning in Healthcare
  • Diabetes Research

Background:

  • Diabetes mellitus (DM) is a chronic, life-threatening disease with severe complications affecting multiple organs.
  • Common DM complications include metabolic syndrome, dyslipidemia, neuropathy, nephropathy, diabetic foot, hypertension, obesity, and retinopathy.

Purpose of the Study:

  • To develop and compare supervised classification models for predicting eight distinct diabetes complications.
  • To evaluate the impact of feature selection on model performance for diabetes complication prediction.

Main Methods:

  • Utilized a dataset of 884 records with 79 features from the Rashid Center for Diabetes and Research (RCDR).
  • Applied data preprocessing for missing values and class imbalance, followed by feature selection (top 5 and 10 features).
  • Employed repeated stratified k-fold cross-validation (k=10, 10 repetitions) and evaluated models using accuracy and F1-score.

Main Results:

  • Achieved maximum accuracy of 97.8% and F1-score of 97.7% in predicting diabetes complications.
  • Demonstrated that models built with a reduced set of selected features can still provide adequate classification performance.
  • Specific record counts for training classifiers varied per complication, e.g., 428 for metabolic syndrome, 836 for dyslipidemia.

Conclusions:

  • Supervised classification algorithms are effective in predicting multiple diabetes complications.
  • Feature selection is a viable strategy to build accurate predictive models while potentially simplifying them.
  • This approach can aid in early detection and management of diabetes-related complications.