在巴西的法律亚马逊地区整合机器学习和用于疟疾病例预测的空间聚类
Kayo Henrique de Carvalho Monteiro1,2, Élisson da Silva Rocha3, Luis Augusto Morais4
1Programa de Pós-graduação em Engenharia da Computação, Universidade de Pernambuco, Pernambuco, Brasil. khcm@ecomp.poli.br.
BMC infectious diseases
|June 8, 2025
概括
这项研究表明,随机森林 (RF) 机器学习最能预测巴西疟疾病例.
科学领域:
- 流行病学 流行病学
- 计算生物学 计算生物学
- 公共卫生 公共卫生
背景情况:
- 疟疾对全球健康构成重大威胁,特别是在巴西的法律亚马逊地区.
- 该地区的环境和社会经济因素有助于疟疾的传播.
- 现有的控制方法不足,需要用于公共卫生干预的先进预测工具.
研究的目的:
- 评估六种计算模型,用于预测巴西法律亚马逊地区每周的疟疾病例.
- 确定该地区疟疾病例预测最有效的模型.
- 评估空间聚类对预测准确性的影响.
主要方法:
- 评估了六种计算模型:长短期记忆 (LSTM),门式循环单位 (GRU),支持向量回归 (SVR),随机森林 (RF),极端梯度增强 (XGBoost) 和自回归集成移动平均线 (ARIMA).
- 模型被用来预测法律亚马逊的多个州的每周疟疾病例.
- 整合了K-means集群,以考虑空间异质性.
主要成果:
- 随机森林 (RF) 模型表现出卓越的性能,在大多数评估区域实现了最低的根平均平方误差 (RMSE) 和平均绝对误差 (MAE).
- 使用RF模型,Acre的集群02的特定结果显示RMSE为0.00203和MAE为0.00133.
- 整合K-means集群增强了机器学习模型的预测准确性.
结论:
- 结合机器学习模型 (特别是RF) 与K-means集群的混合方法为疟疾监测提供了一个强大的工具.
- 这种方法提高了对局部传输动态和空间异质性的理解.
- 这些发现支持加强公共卫生战略,并在高风险地区有针对性的疟疾控制.
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