Using machine learning algorithms to determine the food insecurity level of households of public school children

Cruz, Arnold Dela; Gallegos, Nathaniel Isaiah; Gattud, Kyle Aiden; Antonio, Victor Andrew; Miro, Eden Delight; Go, Clark Kendrick

doi:10.1063/5.0192150

The 7TH BIOMEDICAL ENGINEERING’S RECENT PROGRESS IN BIOMATERIALS, DRUGS DEVELOPMENT, AND MEDICAL DEVICES: The 15th Asian Congre

2024

DOI: 10.1063/5.0192150

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Using machine learning algorithms to determine the food insecurity level of households of public school children

Arnold Dela Cruz,

Nathaniel Isaiah Gallegos,

Kyle Aiden Gattud

et al.

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Machine Learning Approach for Predicting the Impact of Food Insecurity on Nutrient Consumption and Malnutrition in Children Aged 6 Months to 5 Years

Qasrawi,

Sgahir,

Nemer

et al. 2024

Children

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Background: Food 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. Methods: Utilizing 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. Results: The 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. Conclusion: This study provides insights into the differential risks for growth issues among children, offering valuable information for targeted interventions and policymaking.

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Machine Learning Approach for Predicting the Impact of Food Insecurity on Nutrient Consumption and Malnutrition in Children Aged 6 Months to 5 Years

Qasrawi,

Sgahir,

Nemer

et al. 2024

Children

View full text Add to dashboard Cite

show abstract

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Using machine learning algorithms to determine the food insecurity level of households of public school children

Cited by 1 publication

References 12 publications

Machine Learning Approach for Predicting the Impact of Food Insecurity on Nutrient Consumption and Malnutrition in Children Aged 6 Months to 5 Years

Machine Learning Approach for Predicting the Impact of Food Insecurity on Nutrient Consumption and Malnutrition in Children Aged 6 Months to 5 Years

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