Sociodemographic and anthropometric characteristics of children.

Sociodemographic and anthropometric characteristics of children.</p

Top 20 most important variables from the XGBoost model for wasting.

Top 20 most important variables from the XGBoost model for wasting.</p

Receiver operator characteristics on the ML models for stunting on test data.

Receiver operator characteristics on the ML models for stunting on test data.</p

Weighted prevalence of nutritional outcomes by sociodemographic factors.

Weighted prevalence of nutritional outcomes by sociodemographic factors.</p

A sample confusion matrix of binary classifier.

BackgroundUndernutrition among children under the age of five is a major public health concern, especially in developing countries. This study aimed to use machine learning (ML) algorithms to predict undernutrition and identify its associated factors.MethodsSecondary data analysis of the 2017 Multiple Indicator Cluster Survey (MICS) was performed using R and Python. The main outcomes of interest were undernutrition (stunting: height-for-age (HAZ) ResultsIn total, 8564 children were included in the final analysis. The average age of the children was 926 days, and the majority were females. The weighted prevalence rates of stunting, wasting, and underweight were 17%, 7%, and 12%, respectively. The accuracies of all the ML models for wasting were (LDA: 84%; Logistic: 95%; SVM: 92%; RF: 94%; LASSO: 96%; Ridge: 84%, XGBoost: 98%), stunting (LDA: 86%; Logistic: 86%; SVM: 98%; RF: 88%; LASSO: 86%; Ridge: 86%, XGBoost: 98%), and for underweight were (LDA: 90%; Logistic: 92%; SVM: 98%; RF: 89%; LASSO: 92%; Ridge: 88%, XGBoost: 98%). The AUC values of the wasting models were (LDA: 99%; Logistic: 100%; SVM: 72%; RF: 94%; LASSO: 99%; Ridge: 59%, XGBoost: 100%), for stunting were (LDA: 89%; Logistic: 90%; SVM: 100%; RF: 92%; LASSO: 90%; Ridge: 89%, XGBoost: 100%), and for underweight were (LDA: 95%; Logistic: 96%; SVM: 100%; RF: 94%; LASSO: 96%; Ridge: 82%, XGBoost: 82%). Age, weight, length/height, sex, region of residence and ethnicity were important predictors of wasting, stunting and underweight.ConclusionThe XGBoost model was the best model for predicting wasting, stunting, and underweight. The findings showed that different ML algorithms could be useful for predicting undernutrition and identifying important predictors for targeted interventions among children under five years in Ghana.</div

Accuracy of Predictive algorithms for child undernutrition indicators on the test data.

Accuracy of Predictive algorithms for child undernutrition indicators on the test data.</p

Receiver operator characteristics on the ML models for wasting test data.

Receiver operator characteristics on the ML models for wasting test data.</p

Top 20 most important variables from the XGBoost model for underweight.

Top 20 most important variables from the XGBoost model for underweight.</p

Top 20 most important variables from the XGBoost model for stunting.

Top 20 most important variables from the XGBoost model for stunting.</p

Receiver operator characteristics on the ML models for underweight on the test data.

Receiver operator characteristics on the ML models for underweight on the test data.</p