通过混合SVM-LR模型提高COVID-19分类准确性
Noor Ilanie Nordin1,2, Wan Azani Mustafa3,4, Muhamad Safiih Lola1,5
1Faculty of Ocean Engineering Technology and Informatics, Universiti Malaysia Terengganu, Kuala Nerus 21030, Terengganu, Malaysia.
Bioengineering (Basel, Switzerland)
|November 25, 2023
概括
结合支持矢量机 (SVM) 和后勤回归 (LR) 的新混合模型,提高了每变量 (EPV) 小事件的预测准确性. 这种机器学习方法为流行病数据分析提供了更好的性能.
科学领域:
- 机器学习 机器学习
- 生物统计学 生物统计学
- 流行病学 流行病学
背景情况:
- 支持矢量机 (SVM) 和物流回归 (LR) 是已建立的分类算法.
- 最近的进步,如包装和组合方法,已经增强了SVM和LR的能力.
- 现有的SVM和LR之间的比较早于这些现代改进.
研究的目的:
- 提出和评估一个新的混合模型,整合SVM和LR.
- 评估混合模型在预测每个变量 (EPV) 的小事件方面的表现.
- 使用真实世界的流行病数据,将混合模型与独立的SVM和LR进行比较.
主要方法:
- 开发一种混合分类模型,将SVM和LR结合起来.
- 对各种EPV值的混合,SVM和LR模型的评估.
- 利用了2019年12月至2020年5月的COVID-19流行病学数据 (WHO).
主要成果:
- 混合SVM-LR模型表现出卓越的分类性能.
- 在精度,平均平方误差 (MSE) 和根平均平方误差 (RMSE) 方面表现优于独立的SVM和LR.
- 在不同EPV级别中观察到一致的性能改善.
结论:
- 拟议的混合模型为流行病学数据提供了增强的预测能力.
- 这种方法对公共卫生当局在管理未来的流行病方面有价值.
- 混合模型为分析具有有限变量的事件提供了一个强大的工具.
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