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

Abstract

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 train the model, and the remaining 20% was used to test the trained model. Model performance was evaluated using sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve. A total of 327,507 under-five children were included in the study. About 7.10, 4.19, 20.61, and 21.02% of children reported symptoms of ARI, Severe ARI, cough, and fever in the 2 weeks preceding the survey years respectively. The prevalence of ARI was highest in Mozambique (15.3%), Uganda (15.05%), Togo (14.27%), and Namibia (13.65%,), whereas Uganda (40.10%), Burundi (38.18%), Zimbabwe (36.95%), and Namibia (31.2%) had the highest prevalence of cough. The results of the random forest plot revealed that spatial locations (longitude, latitude), particulate matter, land surface temperature, nitrogen dioxide, and the number of cattle in the houses are the most important features in predicting the diagnosis of symptoms of ARIs among under-five children in sSA. The RF algorithm was selected as the best ML model (AUC = 0.77, Accuracy = 0.72) to predict the symptoms of ARIs among children under five. The MLA performed well in predicting the symptoms of ARIs and associated predictors among under-five children across the sSA countries. Random forest MLA was identified as the best classifier to be employed for the prediction of the symptoms of ARI among under-five children.

Keywords

• Machine learning
• Acute respiratory infection
• Demographic health survey
• Feature selection