Xu Deren, Chan Weng Howe, Haron Habibollah
Faculty of Computing, Universiti Teknologi Malaysia, Faculty of Computing, Johor, Johor Bahru, Malaysia.
Universiti Teknologi Malaysia, UTM Big Data Centre, Ibnu Sina Institute For Scientific and Industrial Resarch, Universiti Teknologi Malaysia, Johor, Johor Bahru, Malaysia.
PeerJ Comput Sci. 2024 Jul 29;10:e2217. doi: 10.7717/peerj-cs.2217. eCollection 2024.
As the pandemic continues to pose challenges to global public health, developing effective predictive models has become an urgent research topic. This study aims to explore the application of multi-objective optimization methods in selecting infectious disease prediction models and evaluate their impact on improving prediction accuracy, generalizability, and computational efficiency. In this study, the NSGA-II algorithm was used to compare models selected by multi-objective optimization with those selected by traditional single-objective optimization. The results indicate that decision tree (DT) and extreme gradient boosting regressor (XGBoost) models selected through multi-objective optimization methods outperform those selected by other methods in terms of accuracy, generalizability, and computational efficiency. Compared to the ridge regression model selected through single-objective optimization methods, the decision tree (DT) and XGBoost models demonstrate significantly lower root mean square error (RMSE) on real datasets. This finding highlights the potential advantages of multi-objective optimization in balancing multiple evaluation metrics. However, this study's limitations suggest future research directions, including algorithm improvements, expanded evaluation metrics, and the use of more diverse datasets. The conclusions of this study emphasize the theoretical and practical significance of multi-objective optimization methods in public health decision support systems, indicating their wide-ranging potential applications in selecting predictive models.
随着这一全球大流行疾病持续对全球公共卫生构成挑战，开发有效的预测模型已成为一个紧迫的研究课题。本研究旨在探索多目标优化方法在传染病预测模型选择中的应用，并评估其对提高预测准确性、泛化能力和计算效率的影响。在本研究中，使用NSGA-II算法将通过多目标优化选择的模型与通过传统单目标优化选择的模型进行比较。结果表明，通过多目标优化方法选择的决策树（DT）模型和极端梯度提升回归模型（XGBoost）在准确性、泛化能力和计算效率方面优于通过其他方法选择的模型。与通过单目标优化方法选择的岭回归模型相比，决策树（DT）模型和XGBoost模型在真实数据集上的均方根误差（RMSE）显著更低。这一发现凸显了多目标优化在平衡多个评估指标方面的潜在优势。然而，本研究的局限性也为未来的研究方向提供了指引，包括算法改进、扩展评估指标以及使用更多样化的数据集。本研究的结论强调了多目标优化方法在公共卫生决策支持系统中的理论和实践意义，表明其在预测模型选择方面具有广泛的潜在应用。
