Prediction of Diabetes at Early Stage using Interpretable Machine Learning
Diabetes, for a long period of time, was misjudged as a trivial concerned disease but has
now risen to become one of the fastest-growing chronic diseases, affecting around 463 …
now risen to become one of the fastest-growing chronic diseases, affecting around 463 …
Machine learning techniques for the identification of risk factors associated with food insecurity among adults in Arab countries during the COVID-19 pandemic
Background 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 …
physical and mental health, food insecurity is a rising global concern associated with low …
Risk Propensity and Acceptance of Gene-edited and Genetically Modified Food among US Consumers: A Comparison between Plants and Animal Products
Utilizing online survey data of US consumers, this study examines the extent to which
consumers' acceptance of genetically modified (GM) and gene-edited (GE) food is driven by …
consumers' acceptance of genetically modified (GM) and gene-edited (GE) food is driven by …
[PDF][PDF] A comparison of machine learning and deep learning models for predicting household food security status
M Nigus, HL Shashirekha - IJEER, 2022 - academia.edu
░ ABSTRACT-ML and DL algorithms are becoming more popular to predict household food
security status, which can be used by the governments and policymakers of the country to …
security status, which can be used by the governments and policymakers of the country to …
Investigating the Association between Nutrient Intake and Food Insecurity among Children and Adolescents in Palestine Using Machine Learning Techniques
Food insecurity is a public health concern that affects children worldwide, yet it represents a
particular burden for low-and middle-income countries. This study aims to utilize machine …
particular burden for low-and middle-income countries. This study aims to utilize machine …
[PDF][PDF] A Geospatial Analysis of Food Insecurity Among Refugee Households in Lebanon Using Machine Learning Techniques
A Lyons, J Kass-Hanna, D Pingali, A Soliman… - Available at SSRN …, 2024 - erf.org.eg
This study integrates geospatial analysis with machine learning to understand the interplay
and spatial dependencies among various indicators of food insecurity. Combining …
and spatial dependencies among various indicators of food insecurity. Combining …
Prediction of household food security status using ensemble learning models
M Nigus, HL Shashirekha - International Journal of Sensors …, 2022 - benthamdirect.com
Background: This research uses the Ethiopian HICE survey dataset. Predicting food
insecurity is critical in presenting the household's situation to the appropriate agencies that …
insecurity is critical in presenting the household's situation to the appropriate agencies that …