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通过使用有效且同时基于metaheuristic的特征选择和超参数调整来改进人类菌病易感性映射.

Iman Zandi1, Ali Jafari2, Ali Asghar Alesheikh3

  • 1Department of GIS, School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran.

Acta tropica
|May 19, 2025
PubMed
概括

这项研究使用先进的机器学习为伊朗开发了人类乳头疹易感性地图 (HBSM). 该地图确定了高风险地区,有助于预防和控制这种重要的动物传染病.

关键词:
选择功能选择功能选择.人类菌病易感性绘制地图超参数调整 超参数调整听算法算法的算法支持矢量回归的支持矢量回归

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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:

  • 人类百菌病是一种被忽视的动物传染病,影响全球数百万人.
  • 伊朗面临着相当大的负担,患病率很高.
  • 有效的疾病映射对于控制策略至关重要.

研究的目的:

  • 为伊朗马赞达兰省开发可靠的人类百菌病易感性地图 (HBSM).
  • 提高机器学习模型的性能,用于疾病易感性预测.
  • 确定针对性干预的高风险地区.

主要方法:

  • 一种混合机器学习方法,将支持向量回归 (SVR) 与元启发优化 (灰狼优化器) 结合起来.
  • 整合一个转换函数用于特征和超参数优化.
  • 采用双相突变运算符来改进特征选择.

主要成果:

  • 该SVR-TMGWO模型显示出优越的性能 (RMSE=0.7723,MAE=0.614,R=0.536).
  • 产生的HBSM突出显示了2018年马赞达兰省68个农村地区的高和非常高易感度等级.
  • 该模型有效地降低了计算复杂性,并改善了功能选择.

结论:

  • 开发的HBSM为马桑达兰省的公共卫生决策者提供了有价值的工具.
  • 混合机器学习方法提供了一种可靠的方法来预测动物传播疾病的易感性.
  • 基于易感性地图,可以更有效地实施有针对性的预防和控制措施.