Predicting and identifying factors associated with undernutrition among children under five years in Ghana using machine learning algorithms.

ABSTRACT

BACKGROUND: Undernutrition 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. METHODS: Secondary 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) < -2 SD; wasting weight-for-height (WHZ) < -2 SD; and underweight weight-for-age (WAZ) < -2 SD). Seven ML algorithms were trained and tested linear discriminant analysis (LDA), logistic model, support vector machine (SVM), random forest (RF), least absolute shrinkage and selection operator (LASSO), ridge regression, and extreme gradient boosting (XGBoost). The ML models were evaluated using the accuracy, confusion matrix, and area under the curve (AUC) receiver operating characteristics (ROC). RESULTS: In 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. CONCLUSION: The 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.