Forecasting of non-accidental, cardiovascular, and respiratory mortality with environmental exposures adopting machine learning approaches

Research output: Contribution to journalJournal articleResearchpeer-review

Environmental exposure constantly changes with time and various interactions that can affect health outcomes. Machine learning (ML) or deep learning (DL) algorithms have been used to solve complex problems, such as multiple exposures and their interactions. This study developed predictive models for cause-specific mortality using ML and DL algorithms with the daily or hourly measured meteorological and air pollution data. The ML algorithm improved the performance compared to the conventional methods, even though the optimal algorithm depended on the adverse health outcomes. The best algorithms were extreme gradient boosting, ridge, and elastic net, respectively, for non-accidental, cardiovascular, and respiratory mortality with daily measurement; they were superior to the generalized additive model reducing a mean absolute error by 4.7%, 4.9%, and 16.8%, respectively. With hourly measurements, the ML model tended to outperform the conventional models, even though hourly data, instead of daily data, did not enhance the performance in some models. The proposed model allows a better understanding and development of robust predictive models for health outcomes using multiple environmental exposures.

Original languageEnglish
JournalEnvironmental Science and Pollution Research
Pages (from-to)88318–88329
Number of pages12
Publication statusPublished - 2022

Bibliographical note

Correction: 10.1007/s11356-022-22224-4

    Research areas

  • Cardiovascular diseases, Deep learning, Environmental exposures, Machine learning, Respiratory tract diseases, TEMPERATURE

ID: 314622811