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المؤلفQasrawi, Radwan
المؤلفHoteit, Maha
المؤلفTayyem, Reema
المؤلفBookari, Khlood
المؤلفAl Sabbah, Haleama
المؤلفKamel, Iman
المؤلفDashti, Somaia
المؤلفAllehdan, Sabika
المؤلفBawadi, Hiba
المؤلفWaly, Mostafa
المؤلفIbrahim, Mohammed O.
المؤلفPolo, Stephanny Vicuna
المؤلفAl-Halawa, Diala Abu
تاريخ الإتاحة2024-01-29T07:10:08Z
تاريخ النشر2023-09-16
اسم المنشورBMC Public Health
المعرّفhttp://dx.doi.org/10.1186/s12889-023-16694-5
الاقتباسQasrawi, 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.
معرّف المصادر الموحدhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85171420184&origin=inward
معرّف المصادر الموحدhttp://hdl.handle.net/10576/51308
الملخصBackground: 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.
اللغةen
الناشرSpringer Nature
الموضوعArab countries
COVID-19
Food consumption score
Food insecurity
Machine learning
Prediction
العنوانMachine learning techniques for the identification of risk factors associated with food insecurity among adults in Arab countries during the COVID-19 pandemic
النوعArticle
رقم العدد1
رقم المجلد23
ESSN1471-2458
dc.accessType Open Access


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