Predicting bottle-feeding practices among mothers of children aged 0–24 months in Somalia: a machine learning analysis of the 2020 Demographic and Health Survey
Résumé
Background: In Somalia, where infectious diseases and malnutrition pose significant threats to child health, suboptimal infant feeding practices like bottle-feeding are a critical public health concern. This study aimed to determine the prevalence and identify key predictors of bottle-feeding among mothers of children aged 0-24 months using advanced machine learning approaches. Methodology: = 5,416). Eight machine learning algorithms were employed to predict bottle-feeding status based on socioeconomic, demographic, and healthcare-related variables. Model performance was evaluated using accuracy, AUC-ROC, precision, recall, and specificity metrics. Results: The prevalence of bottle-feeding among children aged 0-24 months who fed the bottle milk was 45.72% (95% CI: 44.39-47.05). Key predictors included household wealth (AOR = 0.66 for rich vs. poor), place of delivery (AOR = 0.76 for facility vs. home delivery), and child's sex (AOR = 1.13 for males). The Random Forest model demonstrated superior performance (Accuracy = 73.68%, AUC = 0.802), with geographic region, residence type, and parity emerging as the most important predictive features. Conclusion: Bottle-feeding is remarkably prevalent in Somalia and strongly associated with poverty, limited healthcare access, and sociocultural factors. This practice contradicts WHO recommendations and exposes infants to substantial health risks in Somalia's challenging environment. Recommendations: Targeted interventions should focus on high-prevalence regions, integrate breastfeeding support with poverty reduction programs, improve access to health facilities, and address cultural beliefs through community education. Implementation of predictive models could enhance targeted public health efforts to promote optimal infant feeding practices.
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