一种基于中国经验模式分解的结核病预测混合模型
Ruiqing Zhao1, Jing Liu1, Zhiyang Zhao2
1Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, China.
BMC infectious diseases
|October 7, 2023
概括
与其他模型相比,EMD-ARMA-LSTM模型显著提高了肺结核发病率预测的准确性. 这种先进的预测为有效的结核病预防和控制策略提供了基础.
科学领域:
- 流行病学 流行病学
- 公共卫生 公共卫生
- 时间序列分析时间序列分析
- 机器学习 机器学习
背景情况:
- 肺结核 (TB) 仍然是一个重大的全球卫生挑战.
- 准确的预测模型对于有效的结核病预防和控制至关重要.
- 这项研究解决了改善结核病发病率预测的需要.
研究的目的:
- 开发和评估用于预测肺结核发病率的先进模型.
- 为了比较各种时间序列和机器学习模型的预测性能.
- 确定预测结核病流行趋势的最佳模型.
主要方法:
- 利用来自中国 (2008-2018) 的月度结核病发病率数据.
- 开发并比较了ARIMA,LSTM,EMD-SARIMA,EMD-LSTM和EMD-ARMA-LSTM模型. 这些模型是:
- 使用平均平方误差 (MSE),平均绝对误差 (MAE),根平均平方误差 (RMSE) 和平均绝对百分比误差 (MAPE) 评估模型性能.
主要成果:
- 基于分解的模型 (EMD-SARIMA,EMD-LSTM) 的表现优于未分解的模型.
- 与EMD-SARIMA和EMD-LSTM相比,EMD-ARMA-LSTM模型显示出卓越的预测准确性,大大降低了错误指标.
- 模型性能在各种预测期 (3,6个月和9个月) 中保持一致.
结论:
- 混合模型整合了分解技术和多个算法,提高了预测准确度.
- 该EMD-ARMA-LSTM模型为预测肺结核发病率提供了卓越的性能.
- 研究结果为制定有针对性的结核病预防和控制政策提供了理论基础.
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