Abstract: Childhood obesity is a common and serious public health problem that requires early prevention measures. Identifying children at risk of obesity is crucial for timely interventions that aim to mitigate these adverse health outcomes. Machine learning (ML) offers powerful tools to predict obesity and related complications using large and diverse data sources. The article uses machine learning (ML) techniques to analyze children's data, focusing on a newly developed variable, the Obesity Risk Level (ORL), which categorizes participants into high, medium, and low risk levels. Two primary models were utilized: the K-Means algorithm for clustering participants based on shared characteristics and XGBoost for predicting the risk level and obesity likelihood. The results showed an overall prediction precision of 88.04%, with high precision, recall, and F1 scores, demonstrating the robustness of the model in identifying obesity risks. This approach provides a data-driven framework to improve health interventions and prevent childhood obesity, providing information that could shape future preventive strategies.

Ghaidaa Hamed Alharbi and Mohammed Abdulaziz Ikram, “An Obesity Risk Level (ORL) Based on Combination of K-Means and XGboost Algorithms to Predict Childhood Obesity” International Journal of Advanced Computer Science and Applications(IJACSA), 16(4), 2025. http://dx.doi.org/10.14569/IJACSA.2025.0160440

@article{Alharbi2025,
title = {An Obesity Risk Level (ORL) Based on Combination of K-Means and XGboost Algorithms to Predict Childhood Obesity},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160440},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160440},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {4},
author = {Ghaidaa Hamed Alharbi and Mohammed Abdulaziz Ikram}
}