Predictors of micronutrient deficiency among children aged 6-23 months in Ethiopia: a machine learning approach

Abstract

Introduction: Micronutrient (MN) deficiencies are a major public health problem in developing countries including Ethiopia, leading to childhood morbidity and mortality. Effective implementation of programs aimed at reducing MN deficiencies requires an understanding of the important drivers of suboptimal MN intake. Therefore, this study aimed to identify important predictors of MN deficiency among children aged 6-23 months in Ethiopia using machine learning algorithms.

Methods: This study employed data from the 2019 Ethiopia Mini Demographic and Health Survey (2019 EMDHS) and included a sample of 1,455 children aged 6-23 months for analysis. Machine Learning (ML) methods including, Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), Neural Network (NN), and Naïve Bayes (NB) were used to prioritize risk factors for MN deficiency prediction. Performance metrics including accuracy, sensitivity, specificity, and Area Under the Receiver Operating Characteristic (AUROC) curves were used to evaluate model prediction performance.

Results: The prediction performance of the RF model was the best performing ML model in predicting child MN deficiency, with an AUROC of 80.01% and accuracy of 72.41% in the test data. The RF algorithm identified the eastern region of Ethiopia, poorest wealth index, no maternal education, lack of media exposure, home delivery, and younger child age as the top prioritized risk factors in their order of importance for MN deficiency prediction.

Conclusion: The RF algorithm outperformed other ML algorithms in predicting child MN deficiency in Ethiopia. Based on the findings of this study, improving women's education, increasing exposure to mass media, introducing MN-rich foods in early childhood, enhancing access to health services, and targeted intervention in the eastern region are strongly recommended to significantly reduce child MN deficiency.

Keywords: AUROC; Ethiopia; child micronutrient deficiency; machine learning; spatial variation.

Figure 1

Flow chart of Machine learning approach.

Figure 2

Spatial variations in MN deficiency by administrative regions in Ethiopia, EMDHS, 2019.

Figure 3

ROC curve for machine learning models in predicting childhood micronutrient deficiency. ROC, receiver operating characteristic; AUROC: area under the receiver operating characteristics.

Figure 4

Variable importance from random forest.

Figure 5

The spatial distribution of the actual (A), and predicted (B) of MN deficiency prevalence on the test data. MND, micronutrient deficiency.