机器学习的应用来预测COVID-19通过优化的BPSO模型传播
Eman H Alkhammash1, Sara Ahmad Assiri2, Dalal M Nemenqani3
1Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia.
Biomimetics (Basel, Switzerland)
|October 27, 2023
概括
这项研究开发了一种增强的模型来预测沙特阿拉伯的COVID-19病例,其表现优于以前的方法. 该模型在吉达 (海平面) 实现了更高的准确性,而不是在塔伊夫 (高海拔) 预测COVID-19.
科学领域:
- 流行病学 流行病学
- 机器学习 机器学习
- 计算生物学 计算生物学
背景情况:
- 冠状病毒疾病 (COVID-19) 病例数量在不同地区有很大差异.
- 诸如高度之类的地理因素可能会影响疾病的传播和预测的准确性.
研究的目的:
- 开发和评估沙特阿拉伯COVID-19病例的增强预测模型.
- 为了比较高海拔地区 (Taif) 与海平面 (Jeddah) 地区的模型性能.
主要方法:
- 使用二进制粒子群集优化 (BPSO) 进行特征选择.
- 实施并比较了三种机器学习模型:随机森林,梯度增强和天真贝叶斯.
- 在沙特阿拉伯塔伊夫和吉达的数据集上训练和测试模型.
主要成果:
- 梯度提升在Taif.取得了94.6%的准确性.
- 随机森林在吉达取得了95.5%的准确性.
- 吉达数据集的整体准确性比塔伊夫数据集更好.
结论:
- 改进的模型有效预测沙特阿拉伯不同地区的COVID-19病例.
- 海平面地区 (Jeddah) 的预测准确度高于高海拔地区 (Taif).
- 模型性能因地理位置和选择的机器学习算法而异.
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