Background: There is a dearth of literature on predictive models estimating under-five mortality risk in Ethiopia. In this study, we develop a spatial map and predictive models to predict the sociodemographic determinants of under-five mortality in Ethiopia.
Methods: The study data were drawn from the 2016 Ethiopian Demographic and Health Survey. We used three predictive models to predict under-five mortality within this sample. The three techniques are random forests, logistic regression, and k-nearest neighbors For each model, measures of model accuracy and Receiver Operating Characteristic curves are used to evaluate the predictive power of each model.
Results: There are considerable regional variations in under-five mortality rates in Ethiopia. The under-five mortality prediction ability was found to be moderate to low for the models considered, with the random forest model showing the best performance. Maternal age at birth, sex of a child, previous birth interval, water source, health facility delivery services, antenatal and post-natal care checkups, breastfeeding behavior and household size have been found to be significantly associated with under-five mortality in Ethiopia.
Conclusions: The random forest machine learning algorithm produces a higher predictive power for under-five mortality risk factors for the study sample. There is a need to improve the quality and access to health care services to enhance childhood survival chances in the country.