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Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
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A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...
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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 meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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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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通过多变量分析预测糖尿病是一种基于KNN的创新分类器方法.

B V V Siva Prasad1, Sapna Gupta2, Naiwrita Borah2

  • 1Department of CSE (School of Engineering), Anurag University, Hyderabad, Telangana, India.

Preventive medicine
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概括

这项研究引入了一种可适应的神经模糊推断K-近邻 (AF-KNN) 模型,用于使用患者数据预测糖尿病风险. AF-KNN 方法通过优化 K-Nearest Neighbourhood 算法来提高预测准确性.

关键词:
可适应的模糊化K-最近的邻居 (AF-KNN)糖尿病的预后 糖尿病的预后K-最近的邻居 (KNN)机器学习 (ML) 技术

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科学领域:

  • 生物医学信息学 生物医学信息学
  • 医疗保健中的机器学习
  • 糖尿病研究研究 糖尿病研究

背景情况:

  • 糖尿病是一种慢性代谢障碍,其特征是血糖水平升高.
  • 有效的糖尿病管理需要准确的预后和风险评估.
  • 处理敏感的患者数据需要强大可靠的预测模型.

研究的目的:

  • 开发一个可适应的神经模糊推断K-最近的邻居 (AF-KNN) 学习依赖的预测系统.
  • 提高糖尿病风险评估模型的预测准确度.
  • 为了利用患者的行为特征,提高糖尿病预后.

主要方法:

  • 使用K-最近的邻居 (KNN) 机器学习算法作为基础.
  • 开发了一个可适应的神经模糊推断系统 (AF-KNN),与KNN集成.
  • 优化了KNN框架内的社区比例,以最大限度地减少预测不准确性.

主要成果:

  • 拟议的AF-KNN系统证明了糖尿病风险的预测性能得到改善.
  • 该方法有效地确定了最佳邻近参数,以减少不准确性.
  • 患者的行为特征被成功地纳入,以提高预测.

结论:

  • AF-KNN模型为准确预测糖尿病风险提供了一个有希望的方法.
  • 这种可适应的神经模糊系统提高了机器学习在临床决策中的可靠性.
  • 优化KNN参数对于提高医疗保健应用中的预测准确性至关重要.