Machine learning-based algorithms to identify factors associated with inadequate meal frequency among children aged 6-23 months in Somalia: Evidence from the Somalia Demographic and Health Survey 2020.

Objectives: Inadequate meal frequency (IMF) among children aged 6-23 months remains a pressing public health issue in Somalia, contributing to widespread malnutrition and hindering progress toward Sustainable Development Goals 2 (Zero Hunger) and 3 (Good Health and Well-being). This study investigates the most influential factors associated with IMF to inform targeted public health interventions. Methods: Data from 4066 children were extracted from the 2020 Somalia Demographic and Health Survey, employing Five machine learning algorithms, Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, and Gradient Boosting, and assessed for predictive performance using accuracy and area under the receiver operating characteristic curve (AUC-ROC) metrics. Feature importance was analyzed to identify key predictors of IMF. Results: The prevalence of IMF was alarmingly high at 78.51%. The Gradient Boosting model outperformed other models with an accuracy of 89.55% and an AUC-ROC of 92.77%. Birth order emerged as the most dominant predictor across all models, accounting for 74.07% of the Gini importance in the Gradient Boosting model. Other significant predictors included child age, breastfeeding status, maternal education, household wealth, and region of residence. Conclusion: The high prevalence of IMF highlights an urgent need for targeted interventions. Strategies focusing on families with higher birth order children, maternal education, and poverty reduction may be crucial for improving child nutrition in Somalia. These findings demonstrate the potential of machine learning approaches in informing public health strategies and predictive screening in resource-limited settings.