Enhancing infectious disease prediction model selection with multi-objective optimization: an empirical study.

ABSTRACT: 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

SUBMITTER: Xu D 

PROVIDER: S-EPMC11323180 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature