预测COVID-19疫情的先进预测:利用整体模型,先进的优化和分解技术
Yingyu Yin1, Iman Ahmadianfar2, Faten Khalid Karim3
1School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, 510665, China.
Computers in biology and medicine
|April 28, 2024
概括
这项研究引入了一种用于准确预测COVID-19的新型组合模型. 综合机器学习方法显著提高了流行病模式的预测准确性.
科学领域:
- 流行病学 流行病学
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 准确预测COVID-19对于有效的公共卫生干预至关重要.
- 以前的流行病建模研究仅限于使用单一预测技术.
- 在COVID-19系列中的数据波动对准确的预测构成了重大挑战.
研究的目的:
- 开发和评估一个新的整体框架,以改善COVID-19预测.
- 整合多种机器学习方法,以提高预测准确度.
- 在复杂的流行病预测中解决单一模型方法的局限性.
主要方法:
- 一个整体框架整合了内核回归 (KRidge),深度随机向量函数链接 (dRVFL) 和回归 (L-KRidge-dRVFL-RRidge).
- 使用自适应差异演变和粒子群优化 (A-DEPSO) 的优化.
- 输入变量分解通过变时波器实证模式分解 (TVF-EMD) 和使用光梯度增强机 (LGBM) 的特征选择.
主要成果:
- 拟议的整体模型在意大利 (t+10 = 0.965,t+14 = 0.961) 和波兰 (t+10 = 0.952,t+14 = 0.940) 的COVID-19预测中实现了高相关系数 (R).
- 在这两个案例研究中,该模型与其他模型相比,表现优越.
- 实验结果证实了该模型在复杂的流行病预测场景中的出色表现.
结论:
- 新型组合模型在COVID-19预测中提供了卓越的准确性和弹性.
- 综合方法的性能优于现有模型,为流行病预测提供了更可靠的工具.
- 这一框架在管理和控制传染病爆发方面有很大的应用潜力.
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