Machine learning techniques for the identification of risk factors associated with food insecurity among adults in Arab countries during the COVID?19 pandemic

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dc.authoridRadwan Qasrawi / 0000-0001-8671-7026en_US
dc.authorscopusidRadwan Qasrawi / 57212263325en_US
dc.authorwosidRadwan Qasrawi / AAA-6245-2019en_US
dc.contributor.authorQasrawi, Radwan
dc.contributor.authorHoteit, Maha
dc.contributor.authorTayyem, Reema
dc.contributor.authorBookari, Khlood
dc.contributor.authorAl Sabbah, Haleama
dc.contributor.authorKamel, Iman
dc.contributor.authorDashti, Somaia
dc.contributor.authorAllehdan, Sabika
dc.contributor.authorBawadi, Hiba
dc.contributor.authorWaly, Mostafa
dc.contributor.authorIbrahim, Mohammed O.
dc.contributor.authorThe Regional Corona Cooking Survey Group
dc.contributor.authorPolo, Stephanny Vicuna
dc.contributor.authorAl?Halawa, Diala Abu
dc.date.accessioned2023-10-16T10:23:22Z
dc.date.available2023-10-16T10:23:22Z
dc.date.issued2023en_US
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractBackground A direct consequence of global warming, and strongly correlated with poor physical and mental health, food insecurity is a rising global concern associated with low dietary intake. The Coronavirus pandemic has further aggravated food insecurity among vulnerable communities, and thus has sparked the global conversation of equal food access, food distribution, and improvement of food support programs. This research was designed to identify the key features associated with food insecurity during the COVID-19 pandemic using Machine learning techniques. Seven machine learning algorithms were used in the model, which used a dataset of 32 features. The model was designed to predict food insecurity across ten Arab countries in the Gulf and Mediterranean regions. A total of 13,443 participants were extracted from the international Corona Cooking Survey conducted by 38 different countries during the COVID -19 pandemic. Results The findings indicate that Jordanian, Palestinian, Lebanese, and Saudi Arabian respondents reported the highest rates of food insecurity in the region (15.4%, 13.7%, 13.7% and 11.3% respectively). On the other hand, Oman and Bahrain reported the lowest rates (5.4% and 5.5% respectively). Our model obtained accuracy levels of 70%-82% in all algorithms. Gradient Boosting and Random Forest techniques had the highest performance levels in predicting food insecurity (82% and 80% respectively). Place of residence, age, financial instability, difficulties in accessing food, and depression were found to be the most relevant features associated with food insecurity. Conclusions The ML algorithms seem to be an effective method in early detection and prediction of food insecurity and can profoundly aid policymaking. The integration of ML approaches in public health strategies could potentially improve the development of targeted and effective interventions to combat food insecurity in these regions and globally.en_US
dc.identifier.citationQasrawi, R., Hoteit, M., Tayyem, R., Bookari, K., Al Sabbah, H., Kamel, I., ... & Al-Halawa, D. A. (2023). Machine learning techniques for the identification of risk factors associated with food insecurity among adults in Arab countries during the COVID-19 pandemic. BMC public health, 23(1), 1805.en_US
dc.identifier.doi10.1186/s12889-023-16694-5en_US
dc.identifier.issn1471-2458en_US
dc.identifier.issue1en_US
dc.identifier.pmid37716999en_US
dc.identifier.scopus2-s2.0-85171420184en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1186/s12889-023-16694-5
dc.identifier.urihttps://hdl.handle.net/20.500.12713/3979
dc.identifier.volume23en_US
dc.identifier.wosWOS:001067804900005en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.institutionauthorQasrawi, Radwan
dc.language.isoenen_US
dc.publisherBioMed Centralen_US
dc.relation.ispartofBMC Public Healthen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectFood Insecurityen_US
dc.subjectCOVID-19en_US
dc.subjectFood Consumption Scoreen_US
dc.subjectMachine Learningen_US
dc.subjectPredictionen_US
dc.subjectArab Countriesen_US
dc.titleMachine learning techniques for the identification of risk factors associated with food insecurity among adults in Arab countries during the COVID?19 pandemicen_US
dc.typeArticleen_US

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