Objective: Child undernutrition is a global public health problem with serious implications. In this study, we estimate predictive algorithms for the determinants of childhood stunting by using various machine learning (ML) algorithms. Design: This study draws on data from the Ethiopian Demographic and Health Survey of 2016. Five ML algorithms including eXtreme gradient boosting, k-nearest neighbours (k-NN), random forest, neural network and the generalised linear models were considered to predict the socio-demographic risk factors for undernutrition in Ethiopia. Setting: Households in Ethiopia. Participants: A total of 9471 children below 5 years of age participated in this study. Results: The descriptive results show substantial regional variations in child stunting, wasting and underweight in Ethiopia. Also, among the five ML algorithms, xgbTree algorithm shows a better prediction ability than the generalised linear mixed algorithm. The best predicting algorithm (xgbTree) shows diverse important predictors of undernutrition across the three outcomes which include time to water source, anaemia history, child age greater than 30 months, small birth size and maternal underweight, among others. Conclusions: The xgbTree algorithm was a reasonably superior ML algorithm for predicting childhood undernutrition in Ethiopia compared to other ML algorithms considered in this study. The findings support improvement in access to water supply, food security and fertility regulation, among others, in the quest to considerably improve childhood nutrition in Ethiopia.

High under-five mortality has been identified as a major problem in many developing countries including Ethiopia. The main purpose of this study is to examine the effect of modern contraceptive use on under-five mortality in Ethiopia. The study draws on data from the 2011 and 2016 Ethiopia Demographic and Health Surveys. The Kaplan-Meier survival function was used to demonstrate the survival probabilities of children while a multivariate analysis using the Cox proportional hazards model was used to estimate the under-five mortality risks for various predictors. The results show consistently higher survival probabilities for children of mothers who use modern contraceptives for all survival periods. Significant predictors of under-five mortality include modern contraceptive use, tetanus vaccinations, mother's age, child's sex, parity, postnatal checkup, marital status, and source of drinking water. Modern contraceptive use has a notable implication for the chances of under-five survival in Ethiopia. This underscores the importance of modern contraceptive use in the pursuit of a substantial reduction in under-five mortality in the country.

Background: Stunting has been a major concern in sub-Saharan Africa. However, little evidence exists on the spatiotemporal variations in under-five stunting within a national context. Objective: This paper examines the spatiotemporal variations in under-five stunting and determinants using data from the Ethiopia Demographic and Health Surveys (2000-2016). Methods: Spatial autocorrelation and multilevel logistic regression models were used to conduct the analyses. Results: The stunting prevalence has decreased from 51% to 37%, while the prevalence of severe stunting has decreased by more than half (from 28% to 12%). Wide regional variations in stunting have been consistently observed over the years, which exhibited a higher level of stunting in Tigray (48%), Afar (42%), and Amhara (42%). The results show considerable local and regional variations in under-five stunting levels with diverse patterns of improvements in regional stunting levels over time. Stunting levels were associated with child-level factors such as the sex of a child, birth size, age of a child, birth order, preceding birth interval, and place of birth. Maternal educational attainment, nutritional status, household wealth, toilet facility type, and place of residence were linked to under-five stunting. The regional-level infant mortality rate was associated with under-five stunting. Conclusions: Specially tailored policies and interventions should be devised to address persistent spatial inequalities in stunting by focusing on higher risk populations.

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.

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 machine learning algorithms such as random forest, logistic regression, and Cox-proportional hazard models to predict the sociodemographic risks for under-five mortality in Ethiopia. The Receiver Operating Characteristic curve was used to evaluate the predictive power of the models.Results There are considerable regional variations in under-five mortality rates in Ethiopia. The under-five mortality prediction ability was found to be 88.7% for the random forest model, 68.3% for the logistic regression model, and 68.0% for the Cox-Proportional Hazard model. Maternal age at birth, sex of a child, previous birth interval, water source, contraceptive use, health facility delivery services, antenatal and post-natal care checkups 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.

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.