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

Key Points

Inadequate meal frequency is linked to several factors including maternal education and poverty levels.
Machine learning approaches effectively highlight critical associations within demographic data from Somalia.
The analysis draws on the Somalia Demographic and Health Survey conducted in 2020, ensuring relevant contextual data.
Targeted interventions may significantly improve child nutrition, addressing urgent public health challenges.

Abstract

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.

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

Key Points

Abstract

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