分析和预测COVID-19多变量数据使用深度合奏学习方法
Shruti Sharma1,2, Yogesh Kumar Gupta1, Abhinava K Mishra3
1Department of Computer Science, Banasthali Vidyapith, Tonk 304022, India.
概括
这项研究引入了一个适应梯度长短期记忆 (AGLSTM) 模型,用于准确的COVID-19病例预测. AGLSTM模型实现了99.81%的准确性,有助于疫情应对计划.
科学领域:
- 流行病学 流行病学
- 机器学习 机器学习
- 公共卫生 公共卫生
背景情况:
- COVID-19 疫情对全球经济和医疗保健系统产生了重大影响.
- 准确的预测模型对于有效的医疗保健资源管理和疾病传播预防至关重要.
- 开发一种用于预测COVID-19病例的通用方法对于疫情准备至关重要.
研究的目的:
- 开发一种强大而通用的方法来预测COVID-19阳性病例.
- 协助合作者制定和完善流行病应对策略.
- 提高疾病传播预测模型的准确性和可靠性.
主要方法:
- 提出了使用多变量时间序列数据的自适应梯度长短期记忆 (AGLSTM) 模型.
- 使用卷积神经网络 (CNN) 进行特征提取和自适应的LSTM进行病例预测.
- 通过印度的案例研究评估了该模型,并结合了数据融合和转移学习技术.
主要成果:
- AGLSTM模型表现出卓越的性能,精度为99.81%.
- 该模型需要最小的训练和预测时间.
- 对比分析包括反复神经网络 (RNN),LSTM,LASSO回归,Ada-Boost,光梯度增强和KNN模型.
结论:
- AGLSTM模型为预测COVID-19病例提供了一个高度准确和高效的解决方案.
- 开发的方法可以适应预测未来传染病的出现.
- 这些发现支持在公共卫生危机管理中使用先进的机器学习技术.
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