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对SARS-CoV-2呼吸道感染的相互信息和特征选择的评估

Sekar Kidambi Raju¹, Seethalakshmi Ramaswamy², Marwa M Eid³

¹School of Computing, SASTRA Deemed University, Thanjavur 613401, India.

Bioengineering (Basel, Switzerland)

|July 29, 2023

概括

这项研究开发了一种机器学习模型,用于预测SARS-CoV-2的传播,通过特征选择和可视化技术提高准确性. 准确的预测有助于公共卫生规划和 pandemic管理的资源分配.

科学领域:

计算流行病学计算流行病学
机器学习在公共卫生中的应用
传染病建模传染病模型

背景情况:

准确预测SARS-CoV-2的传播对于有效的公共卫生规划和资源分配至关重要.
现有的模型可能无法完全捕捉病毒传播的随机性质或处理数据的不确定性.
了解各国的流行病动态需要先进的分析工具.

研究的目的:

为SARS-CoV-2呼吸道感染开发一个预测机器学习模型.
通过特征选择和数据可视化来提高预测准确度.
通过机器学习分析各国的流行病动态.

主要方法:

利用随机回归 (SR) 来建模病毒传播动态和数据不确定性.
采用特征选择技术来识别关键预测变量.
应用邻居嵌入 (NE) 和萨蒙映射 (SM) 来可视化高维数据.
评估算法包括神经网络 (NN),决策树 (DT) 和随机森林 (RF) 与亚当优化器 (AD) 和超参数 (HP).

主要成果:

结合预处理数据与ADHPSRNESM的新编排方法显示出高预测准确度.
特征选择显著提高了SARS-CoV-2传播预测的准确性.
数据可视化技术可以更好地解释潜在的流行病模式.

结论:

开发的机器学习模型为SARS-CoV-2呼吸道感染提供了精确的预测工具.
准确的预测可以增强政策制定者和医疗保健专业人员的知情决策.
这项研究有助于改善SARS-CoV-2流行病的管理和控制策略.

关键词:

预测SARS-CoV-2的情况功能选择功能选择机器学习是机器学习.邻居嵌入嵌入邻居嵌入标本映射绘制标本绘制随机回归的随机回归方法

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