A Comparative Analysis of Machine Learning Models for Obesity Prediction

Obesity is a global health challenge with significant implications for public health systems and individual well-being. Predictive modeling using machine learning (ML) offers a powerful approach to identify individuals at risk of obesity and inform early intervention strategies. This study evaluates the performance of ten ML models, including Logistic Regression, Support Vector Machines, Decision Trees, K-Nearest Neighbors, Naive Bayes, Random Forest, Gradient Boosting, AdaBoost, XGBoost, and LightGBM, in predicting obesity using a publicly available dataset. A rigorous preprocessing pipeline, incorporating missing value handling, categorical encoding, normalization, and outlier detection, was applied to ensure data quality and compatibility with ML algorithms. Performance metrics such as accuracy, precision, recall, and F1-score were evaluated using 10-fold stratified cross-validation. Among the models, LightGBM demonstrated the highest test accuracy (99.19%) and F1-score (99.20%), outperforming Gradient Boosting and Random Forest, which also showed competitive results. The study highlights the superior predictive capabilities of ensemble methods while underscoring the trade-offs between model complexity and interpretability. Logistic Regression provided a strong baseline, demonstrating the importance of preprocessing, but was outperformed by advanced ensemble techniques. This research contributes to the growing field of ML-driven healthcare solutions, offering valuable insights into the strengths and limitations of various predictive models. The findings support the integration of advanced ML techniques in public health systems and pave the way for future research on hybrid and explainable models for obesity prediction and management.