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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dc.authorscopusidRadwan Qasrawi / 57212263325
dc.authorwosidRadwan Qasrawi / AAA-6245-2019
dc.contributor.authorQasrawi, Radwan
dc.contributor.authorSgahir, Sabri
dc.contributor.authorNemer, Maysaa
dc.contributor.authorHalaikah, Mousa
dc.contributor.authorBadrasawi, Manal
dc.contributor.authorAmro, Malak
dc.contributor.authorPolo, Stephanny Vicuna
dc.contributor.authorAbu Al-Halawa, Diala
dc.contributor.authorMujahed, Doa'a
dc.contributor.authorNasreddine, Lara
dc.contributor.authorElmadfa, Ibrahim
dc.contributor.authorAtari, Siham
dc.contributor.authorAl-Jawaldeh, Ayoub
dc.date.accessioned2025-04-18T10:48:54Z
dc.date.available2025-04-18T10:48:54Z
dc.date.issued2024
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractBackground: 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.
dc.description.sponsorshipWorld Health Organization
dc.identifier.citationQasrawi, R., Sgahir, S., Nemer, M., Halaikah, M., Badrasawi, M., Amro, M., ... & Al-Jawaldeh, A. (2024). Machine Learning Approach for Predicting the Impact of Food Insecurity on Nutrient Consumption and Malnutrition in Children Aged 6 Months to 5 Years. Children, 11(7), 810.
dc.identifier.doi10.3390/children11070810
dc.identifier.endpage16
dc.identifier.issn2227-9067
dc.identifier.issue7
dc.identifier.pmid39062259
dc.identifier.scopus2-s2.0-85199580904
dc.identifier.scopusqualityQ2
dc.identifier.startpage1
dc.identifier.urihttp://dx.doi.org/10.3390/children11070810
dc.identifier.urihttps://hdl.handle.net/20.500.12713/7196
dc.identifier.volume11
dc.identifier.wosWOS:001276558800001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.institutionauthorQasrawi, Radwan
dc.institutionauthoridRadwan Qasrawi / 0000-0001-8671-7026
dc.language.isoen
dc.publisherMDPI
dc.relation.ispartofChildren-basel
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectFood Insecurity
dc.subjectMalnutrition
dc.subjectWasting
dc.subjectStunting
dc.subjectMachine Learning
dc.subjectPublic Health
dc.titleMachine learning approach for predicting the impact of food insecurity on nutrient consumption and malnutrition in children aged 6 months to 5 years
dc.typeArticle

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