Machine learning predictive modelling for identification of predictors of acute respiratory infection and diarrhoea in Uganda's rural and urban settings.

ABSTRACT: Despite the widely known preventive interventions, the dyad of acute respiratory infections (ARI) and diarrhoea remain among the top global causes of mortality in under- 5 years. Studies on child morbidity have enormously applied "traditional" statistical techniques that have limitations in handling high dimension data, which leads to the exclusion of some variables. Machine Learning (ML) models appear to perform better on high dimension data (dataset with the number of features p (usually correlated) larger than the number of observations N). Using Uganda's 2006-2016 DHS pooled data on children aged 6-59 months, I applied ML techniques to identify rural-urban differentials in the predictors of child's diarrhoea and ARI. I also used ML to identify other omitted variables in the current chi

SUBMITTER: Kananura RM 

PROVIDER: S-EPMC10021828 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature