ArticleChildren (Basel, Switzerland)2024
Machine Learning Approach for Predicting the Impact of Food Insecurity on Nutrient Consumption and Malnutrition in Children Aged 6 Months to 5 Years.
Article in Children (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence in the Management of Malnutrition in Cancer Patients: A Systematic Review.Advances in nutrition (Bethesda, Md.) · 2025Pooled it
- Machine learning-enhanced modeling approach for optimally predicting household level food insecurity in Ethiopia during COVID-19.Scientific reports · 2026Article
- What's on the Plate? Unveiling Food Insecurity and Nutritional Risk Among Preschool-Aged Children in Türkiye.Food science & nutrition · 2026Article
- Predicting the effects of temperature variability on nutritional status of children under five in Sub-Saharan Africa using machine learning.Scientific reports · 2026Article
- Supervised machine learning for classification and prediction of stunting among under-five Egyptian children.BMC pediatrics · 2025Article
- Determinants of Child Growth in Palestine (Ages 5-17): A Structural Equation Modeling Approach to Food Insecurity, Nutrition, and Socioeconomic Factors.Children (Basel, Switzerland) · 2025Article
- RISE: a novel unified framework for feature relevance in malnutrition analytics integrating statistical and expert insights.Frontiers in public health · 2025Article
- A structural equation modeling approach to examine determinants of nutritional status in Palestinian children 6-59 months.PloS one · 2025Article
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Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
backgroundFood insecurity significantly impacts children's health, affecting their development across cognitive, physical, and socio-emotional dimensions. This study explores the impact of food insecurity among children aged 6 months to 5 years, focusing on nutrient intake and its relationship with various forms of malnutrition.
methodsUtilizing machine learning algorithms, this study analyzed data from 819 children in the West Bank to investigate sociodemographic and health factors associated with food insecurity and its effects on nutritional status. The average age of the children was 33 months, with 52% boys and 48% girls.
resultsThe analysis revealed that 18.1% of children faced food insecurity, with household education, family income, locality, district, and age emerging as significant determinants. Children from food-insecure environments exhibited lower average weight, height, and mid-upper arm circumference compared to their food-secure counterparts, indicating a direct correlation between food insecurity and reduced nutritional and growth metrics. Moreover, the machine learning models observed vitamin B1 as a key indicator of all forms of malnutrition, alongside vitamin K1, vitamin A, and zinc. Specific nutrients like choline in the "underweight" category and carbohydrates in the "wasting" category were identified as unique nutritional priorities.
conclusionThis study provides insights into the differential risks for growth issues among children, offering valuable information for targeted interventions and policymaking.
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