Predicting the incidence of infectious diarrhea with symptom surveillance data using a stacking-based ensembled model.

ABSTRACT:

Background

Infectious diarrhea remains a major public health problem worldwide. This study used stacking ensemble to developed a predictive model for the incidence of infectious diarrhea, aiming to achieve better prediction performance.

Methods

Based on the surveillance data of infectious diarrhea cases, relevant symptoms and meteorological factors of Guangzhou from 2016 to 2021, we developed four base prediction models using artificial neural networks (ANN), Long Short-Term Memory networks (LSTM), support vector regression (SVR) and extreme gradient boosting regression trees (XGBoost), which were then ensembled using stacking to obtain the final prediction model. All the models were evaluated with three metrics: mean absolute percentage error (MAPE), root mean square error

SUBMITTER: Wang P 

PROVIDER: S-EPMC10898154 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature