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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.
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.
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.
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