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相关概念视频

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 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.
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基于整体和深度学习方法的智能糖尿病分类和感知框架.

Qazi Waqas Khan1, Khalid Iqbal2, Rashid Ahmad2,3

  • 1Department of Computer Engineering, Jeju National University, South Korea, Jeju-si, Jeju, South Korea.

PeerJ. Computer science
|April 25, 2024
PubMed
概括
此摘要是机器生成的。

这项研究使用机器学习开发了一种早期糖尿病预测系统. 人工神经网络 (ANN) 和随机森林 (RF) 模型在识别糖尿病风险方面取得了超过99%的准确性.

关键词:
人工神经网络的人工神经网络糖尿病 糖尿病 糖尿病机器学习 机器学习在SFS的基础上,SFS是SFS.

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科学领域:

  • 生物医学信息学 生物医学信息学
  • 计算生物学 计算生物学
  • 医疗保健中的机器学习

背景情况:

  • 血糖水平升高会造成严重的健康风险,包括失明,脏,脏和心脏疾病.
  • 糖尿病导致了全球大量的死亡率,糖尿病患者每年平均死亡率为38%.

研究的目的:

  • 开发和评估用于早期糖尿病发病预测的机器学习分类器.
  • 为了确定准确的糖尿病风险评估的关键特征.

主要方法:

  • 雇佣了奇方位,相互信息和顺序特征选择 (SFS) 来进行特征选择.
  • 训练并比较多个分类器:人工神经网络 (ANN),随机森林 (RF),梯度增强 (GB),Tab-Net和支持向量机 (SVM).
  • 在PIMA和早期风险糖尿病数据集上验证了系统.

主要成果:

  • 在PIMA数据集上,ANN实现了99.35%的准确性.
  • 射频模型在早期糖尿病风险数据集上获得了99.36%的准确性.
  • 与用于糖尿病预测的基线机器学习算法相比,提出的方法显示出更高的性能.

结论:

  • 开发的特征选择和分类方法使得糖尿病的准确早期诊断成为可能.
  • 机器学习模型,特别是ANN和RF,在预测糖尿病风险方面表现出很高的有效性.
  • 这些发现表明,自动化糖尿病预测系统取得了重大进展.