<![CDATA[Obesity is a health problem that continues to increase among individuals of productive age and has the potential to reduce quality of life and work productivity. One of the main challenges in obesity risk assessment is the limitation of conventional methods in accurately identifying obesity risk when dealing with complex, multidimensional data that include both numerical and categorical variables. Therefore, an artificial intelligence–based approach is required to provide a more accurate and stable obesity risk classification. This study aims to implement and evaluate a LightGBM–CatBoost ensemble method for obesity risk classification with a focus on the productive age population. The dataset used in this study was obtained from the Kaggle platform and consisted of 2,111 individual records containing physical attributes, eating habits, physical activity, and lifestyle factors. Although the dataset is synthetic and balanced, the included attributes and age-related variables are representative of individuals within the productive age range, making it suitable for modeling obesity risk in this demographic context. The research stages include data preprocessing, separate training of the LightGBM and CatBoost models, model integration using a probability averaging ensemble technique, and performance evaluation using accuracy, precision, recall, and F1-score metrics. The results indicate that both LightGBM and CatBoost achieved accuracy levels above 95%, while the ensemble model demonstrated superior performance with an accuracy of 96.69% and more balanced evaluation metrics across all obesity risk classes. These findings confirm that the ensemble approach improves classification stability and accuracy compared to single models. Therefore, the LightGBM–CatBoost ensemble method is effective for obesity risk classification and has the potential to be further developed as a decision support system in the health sector.]]>
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