Applying XGBoost, Neural Networks, and Oversampling in the Undernutrition Classification of School-Aged Children in the Philippines

Applying XGBoost, Neural Networks, and Oversampling in the Undernutrition Classification of School-Aged Children in the Philippines

In the Philippines, one in five school-aged children are affected by undernutrition, increasing their risk of physical and cognitive development. The Department of Education (DepEd) attempts to address this issue by targeting children with low body mass index (BMI) for their school-based feeding pro...

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Bibliographic Details
Main Authors: Yiu, Mark Kevin A.Ong, Pastor, Carlo Gabriel M., Candano, Gabrielle Jackie C., Miro, Eden Delight, Antonio, Victor Andrew A., Go, Clark Kendrick C
Format: text
Published: Archīum Ateneo 2024
Subjects:
Applied Mathematics
Data Science
Online Access:https://archium.ateneo.edu/mathematics-faculty-pubs/301
https://doi.org/10.1063/5.0213404
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https://archium.ateneo.edu/mathematics-faculty-pubs/301
https://doi.org/10.1063/5.0213404

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