基于优化深度学习算法的传染病预测模型.
Qian Cao1, Junling Zheng2, Yunyue Liu3
1College of Science, North China University of Science and Technology, Tangshan, China.
Frontiers in public health
|January 30, 2026
概括
一个新的混合模型,GA-BiLSTM-ARIMA,通过结合遗传算法,双向长期短期记忆网络和自行回归集成移动平均线模型,准确预测COVID-19趋势. 这种先进的方法为传染病时间序列数据提供了卓越的预测准确性.
科学领域:
- 流行病学和公共卫生.
- 计算生物学和生物信息学
- 数据科学和机器学习
背景情况:
- 由于数据的复杂性,COVID-19大流行凸显了传统算法在准确预测流行病轨迹方面的局限性.
- 现有的模型,如自行回归集成移动平均 (ARIMA) 捕捉基于时间的趋势,而双向长期短期存储器 (BiLSTM) 网络在顺序数据方面表现出色.
- 遗传算法 (GA) 在复杂的预测任务中提供模型选择和参数调整的优化.
研究的目的:
- 开发和评估一种新的混合模型,GA-BiLSTM-ARIMA,用于更好地预测传染病爆发.
- 评估GA-BiLSTM-ARIMA模型与使用COVID-19数据的独立BiLSTM和ARIMA模型的预测性能.
- 证明模型在提高公共卫生决策预测准确度方面的能力.
主要方法:
- 提出了一个混合预测模型,集成遗传算法 (GA) 进行优化,双向长期短期记忆 (BiLSTM) 网络进行连续数据分析,以及自行回归集成移动平均值 (ARIMA) 进行时间序列趋势捕获.
- 利用来自日本的COVID-19病例数据进行模型培训和验证.
- 使用标准指标评估模型性能:根平均平方误差 (RMSE),平均绝对误差 (MAE),平均绝对百分比误差 (MAPE) 和R平方 (R2).
主要成果:
- 与独立的BiLSTM和ARIMA模型相比,GA-BiLSTM-ARIMA模型实现了更高的预测性能.
- 针对GA-BiLSTM-ARIMA的具体评估指标是:RMSE=2,262.42,MAE=1,672.07,MAPE=6.81,R2=0.9764.02,MAE=1.672.07,MAPE=6.81,以及R2=0.9764.2 这两项指标均为 GA-BiLSTM-ARIMA 的具体评估指标.
- 混合战略在预测传染病时间序列方面表现出强大且更高的预测准确性.
结论:
- 该GA-BiLSTM-ARIMA模型通过智能优化有效地结合了单个算法的优势,提供了更准确的预测工具.
- 这种先进的混合模型提供了更可靠的早期预警,并支持开发有效的流行病预防和控制策略.
- 这些发现有助于通过向政策制定者和公众提供准确的流行病信息,更好地应对全球流行病挑战.
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