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马来西亚登革热流行病学混合集群算法的比较稳定性分析,使用拓描述符

Ooi Cheng Jie¹, Nur Fariha Syaqina Zulkepli¹, R U Gobithaasan¹

¹School of Mathematical Sciences, Universiti Sains Malaysia, 11800 USM, Penang, Malaysia.

|August 8, 2025

概括

拓数据分析 (TDA) 为分析马来西亚登革热传播动态提供了一个强大的方法. 这种使用欧勒特征曲线的新方法,与传统集群技术相比,显示出优越的稳定性和抗噪能力.

科学领域:

流行病学流行病学
数据科学数据科学数据科学
公共卫生公共卫生

背景情况:

登革热在马来西亚是一个持续的公共卫生问题,需要先进的分析工具来有效控制.
了解登革热传播动态对于制定有针对性的干预措施和缓解策略至关重要.

研究的目的:

应用拓数据分析 (TDA) 来从登革热流行病学时间序列数据中提取结构特征.
评估基于TDA的集群方法在噪音条件下对传统方法的稳定性和稳定性.

主要方法:

使用欧勒特征曲线的拓数据分析 (TDA) 来分析登革热时间序列数据.
通过在数据中引入受控噪声来进行比较稳定性分析.
采用了外部验证指标,如调整的兰德指数 (ARI),规范互惠信息 (NMI) 和福尔克斯-马洛斯指数 (FMI).

主要成果:

与传统方法相比,基于TDA的集群方法显示出显著增强的稳定性和稳定性,即使数据噪声为30%.
在多个评估指标中观察到实质性的改善:ARI (+252%),NMI (+122.5%),FMI (+67.3%),V-Measure (+122.5%),同质性 (+80%) 和完整性 (+169.7%).
TDA 方法有效地在扰乱下保存了有意义的集群结构,超过了传统的集群算法.

结论:

拓数据分析 (TDA) 提供了一种耐噪声和稳定的方法来分析流行病学数据,特别是为了了解疾病传播模式.
这种增强的稳定性有助于更深入地了解疾病动态,支持更有效的数据驱动的公共卫生分析和登革热干预策略.

关键词:

集群集成是指集群集成.聚类算法聚类算法聚类算法登革热是因为登革热.欧勒的特征是欧勒的特征.拓学数据分析的分析.

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