通过多目标优化增强传染病预测模型选择:一项实证研究
Deren Xu1, Weng Howe Chan2, Habibollah Haron1
1Faculty of Computing, Universiti Teknologi Malaysia, Faculty of Computing, Johor, Johor Bahru, Malaysia.
PeerJ. Computer science
|August 15, 2024
概括
多目标优化有效地选择传染病预测模型,提高准确性和效率. 决策树和XGBoost模型在公共卫生应用中表现优于传统方法.
科学领域:
- 公共卫生 公共卫生
- 计算生物学 计算生物学
- 优化方法 优化方法
背景情况:
- 全球公共卫生挑战需要先进的传染病预测模型.
- 选择最佳模型对于准确的预测和资源分配至关重要.
研究的目的:
- 研究多目标优化在选择传染病预测模型中的应用.
- 评估多目标优化对预测准确性,概括性和计算效率的影响.
主要方法:
- 使用NSGA-II算法进行多目标优化.
- 通过多目标优化选择的模型与通过单目标优化选择的模型进行了比较.
- 评估的模型包括决策树 (DT) 和极端梯度增强回归器 (XGBoost).
主要成果:
- 多目标优化选择了DT和XGBoost模型,这些模型在准确性,概括性和效率方面表现优于其他模型.
- 与单一目标方法选择的回归相比,DT和XGBoost模型显示出明显较低的根平均平方误差 (RMSE).
- 证明了多目标优化在平衡多个绩效指标方面的有效性.
结论:
- 多目标优化为选择传染病预测模型提供了显著的优势.
- 研究结果强调了这些方法在公共卫生决策支持系统中的理论和实践重要性.
- 未来的研究应该探索算法增强,更广泛的指标和多样化的数据集.
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