Dataset Information

Machine learning algorithms for predicting undernutrition among under-five children in Ethiopia.

ABSTRACT:

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.

SUBMITTER: Bitew FH

PROVIDER: S-EPMC8883776 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Machine learning algorithms for predicting undernutrition among under-five children in Ethiopia.

Bitew Fikrewold H FH Sparks Corey S CS Nyarko Samuel H SH

Public health nutrition 20211008 2

<h4>Objective</h4>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.<h4>Design</h4>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 consid ...[more]

PMID: 34620263

Similar Datasets

Project description:BackgroundAlthough the prevalence of childhood illnesses has significantly decreased, acute respiratory infections continue to be the leading cause of death and disease among children in low- and middle-income countries. Seven percent of children under five experienced symptoms in the two weeks preceding the Ethiopian demographic and health survey. Hence, this study aimed to identify interpretable predicting factors of acute respiratory infection disease among under-five children in Ethiopia using machine learning analysis techniques.MethodsSecondary data analysis was performed using 2016 Ethiopian demographic and health survey data. Data were extracted using STATA and imported into Jupyter Notebook for further analysis. The presence of acute respiratory infection in a child under the age of 5 was the outcome variable, categorized as yes and no. Five ensemble boosting machine learning algorithms such as adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), Gradient Boost, CatBoost, and light gradient-boosting machine (LightGBM) were employed on a total sample of 10,641 children under the age of 5. The Shapley additive explanations technique was used to identify the important features and effects of each feature driving the prediction.ResultsThe XGBoost model achieved an accuracy of 79.3%, an F1 score of 78.4%, a recall of 78.3%, a precision of 81.7%, and a receiver operating curve area under the curve of 86.1% after model optimization. Child age (month), history of diarrhea, number of living children, duration of breastfeeding, and mother's occupation were the top predicting factors of acute respiratory infection among children under the age of 5 in Ethiopia.ConclusionThe XGBoost classifier was the best predictive model with improved performance, and predicting factors of acute respiratory infection were identified with the help of the Shapely additive explanation. The findings of this study can help policymakers and stakeholders understand the decision-making process for acute respiratory infection prevention among under-five children in Ethiopia.

Project description:IntroductionDespite the availability of a safe and effective measles vaccine in Ethiopia, the country has experienced recurrent and significant measles outbreaks, with a nearly fivefold increase in confirmed cases from 2021 to 2023. The WHO has identified being unvaccinated against measles as a major factor driving this resurgence of cases and deaths. Consequently, this study aimed to apply robust machine learning algorithms to predict the key factors contributing to measles vaccination dropout.MethodsThis study utilised data from the 2016 Ethiopian Demographic and Health Survey to evaluate measles vaccination dropout. Eight supervised machine learning algorithms were implemented: eXtreme Gradient Boosting (XGBoost), Random Forest, Gradient Boosting, Support Vector Machine, Decision Tree, Naïve Bayes, K-Nearest Neighbours and Logistic Regression. Data preprocessing and model development were performed using R language V.4.2.1. The predictive models were evaluated using accuracy, precision, recall, F1-score and area under the curve (AUC). Unlike previous studies, this research utilised Shapley values to interpret individual predictions made by the top-performing machine learning model.ResultsThe XGBoost algorithm surpassed all classifiers in predicting measles vaccination dropout (Accuracy and AUC values of 73.9% and 0.813, respectively). The Shapley Beeswarm plot displayed how each feature influenced the best model's predictions. The model predicted that the younger mother's age, religion-Jehovah/Adventist, husband with no and mother with primary education, unemployment of the mother, residence in the Oromia and Somali regions, large family size and older paternal age have a strong positive impact on the measles vaccination dropout.ConclusionThe measles dropout rate in the country exceeded the recommended threshold of <10%. To tackle this issue, targeted interventions are crucial. Public awareness campaigns, regular health education and partnerships with religious institutions and health extension workers should be implemented, particularly in the identified underprivileged regions. These measures can help reduce measles vaccination dropout rates and enhance overall coverage.

Dataset Information

Machine learning algorithms for predicting undernutrition among under-five children in Ethiopia.

Objective

Design

Setting

Participants

Results

Conclusions

Publications

Machine learning algorithms for predicting undernutrition among under-five children in Ethiopia.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets