基于贝叶斯网络和协会规则挖矿的公共紧急情况的互联网公众意见的风险识别和评估
Mengjie You1, Xuwei Pan1, Chuwen Zhu1
1School of Economics Management, Zhejiang Sci-Tech University, Hangzhou, China.
Frontiers in public health
|August 12, 2025
概括
本研究引入了一种结合关联规则挖掘和贝叶斯网络 (BN) 的新方法,以改进公共紧急风险评估. 增强方法准确地识别了关键的论风险及其相互作用,有助于危机管理.
科学领域:
- 公共卫生 公共卫生
- 信息科学 信息科学 信息科学
- 风险管理 风险管理
背景情况:
- 全球公共紧急情况频繁发生,增加了互联网论危机的风险.
- 在紧急情况下评估互联网论风险对危机管理效率和响应速度具有挑战性.
- 现有的方法难以处理复杂的数据并识别复杂的风险因素相关性.
研究的目的:
- 开发一种创新的方法来识别和评估公共紧急情况中的互联网论风险.
- 通过数据驱动的洞察力,提高公共危机管理的准确性和效率.
- 为管理论危机提供可解释和计算效率高的决策支持工具.
主要方法:
- 协会规则采矿和贝叶斯网络 (BN) 的整合用于风险评估.
- 开发一个改进的基于关联的分类 (CBA) 算法来处理非布尔数据.
- 构建一个可解释的BN模型,以确定关键的风险因素和链.
主要成果:
- 与传统的BN模型相比,拟议的方法在评估准确度上实现了14.4%的提高.
- 在精度,回忆,特异性和F测量方面表现出卓越的性能.
- 成功确定了关键的论风险因素和复杂的相互作用链.
结论:
- 这种新方法显著提高了公共紧急情况期间公众论风险评估的准确性.
- 为理解风险因素之间的复杂相互作用提供了数据驱动的方法.
- 为公众论危机管理提供了一个可解释和有效的决策支持工具.
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