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

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. This study aimed to identify important predictors of MN deficiency among children ages 6-23 months in Ethiopia using machine learning algorithms. 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, Logistic Regression (LR), Random Forest (RF), Naive Bayes (NB), Support Vector Machine (SVM), and Neural Network (NN) 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. 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.

Frontiers in Nutrition Gebeye et al. January 2024
  • East and Southern Africa
  • Research
  • Case study