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As seen in: Biomed Central (BMC), F1000Research
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Articles by Chemical Administrations

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 | Full Text

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By Al Sabbah, Stephanny Vicuna, Diala Abu, Chemical Administrations
| Biomed Central (BMC)
Food insecurity and food inequity have been increasingly relevant during and following the COVID-19 pandemic. Millions of households around the world relied on food support programs during COVID-19 [8, 43]. The lockdown-imposed work and movement limitations that significantly increased the risk of food insecurity at the global level [9, 11]. In this paper, we used ML models to predict food insecurity using a dataset containing relevant data from 10 Arab countries during the COVID-19 pandemic.
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F1000Research Article: Assessment of the knowledge, attitudes and practices of Lebanese shoppers towards food labeling: The first steps in the Nutri-score roadmap.

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By Maha Hoteit, Heba Abdul, Chemical Administrations, Nour Yazbeck
| F1000Research
Introduction Non-communicable diseases (NCDs) account for over 35 million disease cases per year and two-thirds of the world's deaths.1 Nutrition-related non-communicable diseases (N-NCDs), mainly diabetes, cancer, hypertension, and cardiovascular diseases, are highly prevalent in most countries in the Eastern Mediterranean Region (EMR).2 In Lebanon, it is estimated that NCDs accounted for 91% of all deaths in 2016.3 Thus, as a preventive action plan, the Lebanese Ministry of Public Health...
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