Machine Learning-Based Prediction Model of Obesity Risk Among adolescents in Central Ethiopia Regional State

Abstract

This study discovers the application of machine learning techniques to predict obesity risk among adolescents aged 10 - 19 years in Central Ethiopia Regional State. It uses a comprehensive dataset composed from above 10,000 adolescents. The methodology involves training multiple supervised machines learning classifiers such as Decision Trees, Support Vector Machines, Random Forest, and XGBoost using balanced and processed datasets. Metrics like as accuracy, precision, recall, and F1-score were used to assess the model's performance using 10-fold cross-validation. The findings demonstrate that Random Forest and XGBoost outperform other models with good predictive accuracy and stability, demonstrating their usefulness for classifying obesity in adolescents. The findings underscore the significance of combining behavioral, biological, and environmental data for efficient risk stratification and provide important insights into the predictive variables of teenage obesity in Ethiopia.