Using Machine Learning Algorithms to Determine the Food Insecurity Level of Households of Public School Children
Hunger and food insecurity continue to rise in the shadow of the COVID-19 pandemic affecting many vulnerable groups, especially children. As food is one of many fundamental human rights, looking into the problem contributes to helping uphold this basic right. Using survey data collected from househo...
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2024
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ph-ateneo-arc.mathematics-faculty-pubs-12522024-04-15T07:37:10Z Using Machine Learning Algorithms to Determine the Food Insecurity Level of Households of Public School Children Cruz, Arnold Dela Gallegos, Nathaniel Isaiah Gattud, Kyle Aiden Antonio, Victor A Miro, Eden Delight Go, Clark C Hunger and food insecurity continue to rise in the shadow of the COVID-19 pandemic affecting many vulnerable groups, especially children. As food is one of many fundamental human rights, looking into the problem contributes to helping uphold this basic right. Using survey data collected from households of public school children in a rural province and in a highly-urbanized city in the Philippines, we aim to compare three machine learning models, namely, logistic regression, support vector machine, and random forest, to predict the level of household food security based on geographic, household, and individual factors. A systematic assessment of the algorithms was performed by using accuracy, precision, recall, and F1-score, which showed that logistic regression algorithm performed best in predicting levels of food insecurity among Filipino households. This study shows that ML-based predictive models can potentially identify the food insecurity levels of a household, which can be used in improving the targeting mechanisms of nutrition-sensitive and nutrition-specific programs. 2024-03-07T08:00:00Z text https://archium.ateneo.edu/mathematics-faculty-pubs/251 https://doi.org/10.1063/5.0192150 Mathematics Faculty Publications Archīum Ateneo Applied Mathematics Food Security Mathematics Physical Sciences and Mathematics |
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Applied Mathematics Food Security Mathematics Physical Sciences and Mathematics Cruz, Arnold Dela Gallegos, Nathaniel Isaiah Gattud, Kyle Aiden Antonio, Victor A Miro, Eden Delight Go, Clark C Using Machine Learning Algorithms to Determine the Food Insecurity Level of Households of Public School Children |
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Hunger and food insecurity continue to rise in the shadow of the COVID-19 pandemic affecting many vulnerable groups, especially children. As food is one of many fundamental human rights, looking into the problem contributes to helping uphold this basic right. Using survey data collected from households of public school children in a rural province and in a highly-urbanized city in the Philippines, we aim to compare three machine learning models, namely, logistic regression, support vector machine, and random forest, to predict the level of household food security based on geographic, household, and individual factors. A systematic assessment of the algorithms was performed by using accuracy, precision, recall, and F1-score, which showed that logistic regression algorithm performed best in predicting levels of food insecurity among Filipino households. This study shows that ML-based predictive models can potentially identify the food insecurity levels of a household, which can be used in improving the targeting mechanisms of nutrition-sensitive and nutrition-specific programs. |
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text |
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Cruz, Arnold Dela Gallegos, Nathaniel Isaiah Gattud, Kyle Aiden Antonio, Victor A Miro, Eden Delight Go, Clark C |
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Cruz, Arnold Dela Gallegos, Nathaniel Isaiah Gattud, Kyle Aiden Antonio, Victor A Miro, Eden Delight Go, Clark C |
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Cruz, Arnold Dela |
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Using Machine Learning Algorithms to Determine the Food Insecurity Level of Households of Public School Children |
title_short |
Using Machine Learning Algorithms to Determine the Food Insecurity Level of Households of Public School Children |
title_full |
Using Machine Learning Algorithms to Determine the Food Insecurity Level of Households of Public School Children |
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Using Machine Learning Algorithms to Determine the Food Insecurity Level of Households of Public School Children |
title_full_unstemmed |
Using Machine Learning Algorithms to Determine the Food Insecurity Level of Households of Public School Children |
title_sort |
using machine learning algorithms to determine the food insecurity level of households of public school children |
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Archīum Ateneo |
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2024 |
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https://archium.ateneo.edu/mathematics-faculty-pubs/251 https://doi.org/10.1063/5.0192150 |
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