Predicting Undernutrition Among Elementary Schoolchildren in the Philippines Using Machine Learning Algorithms

• Main Authors: Siy Van, Vanessa T., Antonio, Victor A, Siguin, Carmina P, Gordoncillo, Normahitta P., Sescon, Joselito T., Go, Clark C, Miro, Eden Delight
• Format: text
• Published: Archīum Ateneo 2022
• Subjects: nutrition; machine learning; Philippines; Random Forest; prediction; energy; classification; Demography, Population, and Ecology; Maternal and Child Health; Mathematics; Nutrition
• Online Access: https://archium.ateneo.edu/mathematics-faculty-pubs/176; https://www.sciencedirect.com/science/article/abs/pii/S0899900721004330#:~:text=Machine%20learning%20algorithms%20can%20predict%20inadequate%20dietary%20intake%20among%20children.&text=Undernutrition%20prevalence%20was%20higher%20using%20international%20versus%20Philippine%20standards.&text=Random%20forest%20was%20most%20accurate%20in%20predicting%20undernutrition%20among%20schoolchildren.
• Institution: Ateneo De Manila University

Objectives This study aimed to compare the accuracy of four machine-learning (ML) algorithms, using two classification schemes, to predict undernutrition based on individual and household risk factors. Methods Data on public-school children were collected from a rural province (310 children) and a...