An Enhanced K-Nearest Neighbors (KNN) Algorithm for Predicting Child Malnutrition in The City of Manila

Raizen Joyce R. Daguplo; Khatalyn E. Mata; Vivien A. Agustin

doi:10.36948/ijfmr.2025.v07i02.33454

An Enhanced K-Nearest Neighbors (KNN) Algorithm for Predicting Child Malnutrition in The City of Manila

Author(s)	Raizen Joyce R. Daguplo, Khatalyn E. Mata, Vivien A. Agustin
Country	Philippines
Abstract	Malnutrition among children in third-world countries like the Philippines remains a critical issue addressed by the UN’s Zero Hunger goal. Traditional methods such as K-Nearest Neighbor face limitations in accuracy due to bias. This study aims to enhance K-Nearest Neighbor’s predictive accuracy (KNN) by incorporating Gaussian kernel similarity and normalization techniques to predict malnutrition and BMI categories. Results show that Enhanced KNN consistently outperforms Basic KNN across various K values, achieving an average of 94.33% for bi-class (Malnutrition/Normal) and 92.67% for multi-class (BMI category) compared to Basic KNN's accuracy results: 87.43% for bi-class and 82.51% for multi-class. The Enhanced algorithm performs better across all metrics, averaging an improvement of 8%. Findings revealed that the use of Gaussian kernel enhanced the accuracy of K-Nearest Neighbor because it prioritizes nearer neighbors over distant ones, which reduces the influence of farther points. This enhancement proved that it is effective in malnutrition prediction even without anthropometric measurements, highlighting its potential to address malnutrition in constrained settings.
Keywords	KNN, Machine Learning, Prediction, Algorithm, Malnutrition, Manila, Philippines
Field	Computer Applications
Published In	Volume 7, Issue 2, March-April 2025
Published On	2025-03-05
DOI	https://doi.org/10.36948/ijfmr.2025.v07i02.33454
Short DOI	https://doi.org/g87c2k

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International Journal For Multidisciplinary Research

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An Enhanced K-Nearest Neighbors (KNN) Algorithm for Predicting Child Malnutrition in The City of Manila

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