Factors of acute respiratory infection among under-five children across sub-Saharan African countries using machine learning approaches.

ABSTRACT: Symptoms of Acute Respiratory infections (ARIs) among under-five children are a global health challenge. We aimed to train and evaluate ten machine learning (ML) classification approaches in predicting symptoms of ARIs reported by mothers among children younger than 5 years in sub-Saharan African (sSA) countries. We used the most recent (2012-2022) nationally representative Demographic and Health Surveys data of 33 sSA countries. The air pollution covariates such as global annual surface particulate matter (PM 2.5) and the nitrogen dioxide available in the form of raster images were obtained from the National Aeronautics and Space Administration (NASA). The MLA was used for predicting the symptoms of ARIs among under-five children. We randomly split the dataset into two, 80% was used to tr

SUBMITTER: Fenta HM 

PROVIDER: S-EPMC11233665 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature