Comparison of ARIMA model, DNN model and LSTM model in predicting disease burden of occupational pneumoconiosis in Tianjin, China

BACKGROUND: This study aims to explore appropriate model for predicting the disease burden of pneumoconiosis in Tianjin by comparing the prediction effects of Autoregressive Integrated Moving Average (ARIMA) model, Deep Neural Networks (DNN) model and multivariate Long Short-Term Memory Neural Netwo...

Descripción completa

Detalles Bibliográficos
Autores principales: Lou, He-Ren, Wang, Xin, Gao, Ya, Zeng, Qiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9694549/
https://www.ncbi.nlm.nih.gov/pubmed/36434563
http://dx.doi.org/10.1186/s12889-022-14642-3

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9694549/
https://www.ncbi.nlm.nih.gov/pubmed/36434563
http://dx.doi.org/10.1186/s12889-022-14642-3

Ejemplares similares