基于机器学习优化算法,预测安省结核病发病率的趋势,2013-2023年
Yan Zhang1, Huan Ma2, Hua Wang3
1Third Department of Tuberculosis, Anhui Chest Hospital, 397 Jixi Road, Shushan, Hefei, 230000, China.
BMC pulmonary medicine
|October 27, 2024
概括
准确的结核病发病率预测对于公共卫生至关重要. 使用多源数据的新型RRL-PSO-MiLSTM模型取得了优异的预测结果,为传染病分析提供了新的基准.
科学领域:
- 公共卫生 公共卫生
- 流行病学 流行病学
- 数据科学数据科学数据科学
背景情况:
- 结核病 (TB) 是一个主要的全球性传染病问题.
- 准确预测结核病发病率仍然是一个重大挑战.
- 有效的预测对于资源分配和干预策略至关重要.
研究的目的:
- 开发和验证一种新的时间序列分析工具,用于预测结核病发病率趋势.
- 使用多源数据融合,提高结核病发病率预测的准确性.
- 为传染病趋势预测和影响因素分析建立一个新的基准.
主要方法:
- 实现随机森林 (RF),递归特征消除 (RFE) 和LASSO用于特征选择.
- 开发了一个集成模型:RF-RFE-LASSO与粒子群优化和多输入长短期存储器 (RRL-PSO-MiLSTM).
- 利用多来源数据进行时间序列分析和预测安省 (2013-2023) 结核病发病率.
主要成果:
- 与传统模型相比,RRL-PSO-MiLSTM模型表现出优异的预测性能.
- 在测试组中取得了优异的结果:MSE: 42.3555,MAE: 59.3333,RMSE: 146.7237,MAPE: 2.1133,R2: 0.8634.33,在测试组中取得了优异的结果.
- 该模型有效地整合了多源数据,以进行可靠的时间序列预测.
结论:
- RRL-PSO-MiLSTM模型在传染病时间序列预测方面取得了重大进展.
- 这种方法为分析传染病趋势和影响因素提供了有价值的新基准.
- 该方法作为公共卫生发病率预测的参考.
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