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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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
格式:	text
出版:	Archīum Ateneo 2024
主題:	Applied Mathematics Data Science
在線閱讀:	https://archium.ateneo.edu/mathematics-faculty-pubs/301 https://doi.org/10.1063/5.0213404
標簽:	添加標簽沒有標簽, 成為第一個標記此記錄!

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