Interpretable diabetes risk prediction: a comparative study of tree-based algorithms using SHAP and LIME

The global burden of diabetes mellitus continues to increase, particularly in low- and middle-income countries. Prior studies have explored the use of machine learning algorithms to predict the risk of this disease; however, evidence from Latin American populations remains limited. This study aims to predict the risk of diabetes mellitus using data from Ecuador and tree-based algorithms, including Gradient Boosting, Extreme Gradient Boosting, Light Gradient Boosting Machine, and CatBoost. The dataset comprises sociodemographic variables, anthropometric measurements, dietary and physical activity habits, clinical indicators, and family history of diabetes, among others. Extreme Gradient Boosting demonstrated the best balance between training and testing performance, achieving an area under the curve (AUC) of 0.99 and 0.96, respectively. Additionally, explainable artificial intelligence techniques, specifically SHAP and LIME, were applied to assess the contribution of variables to the prediction task. The results indicate that elevated blood glucose levels, family history of diabetes, abdominal circumference, age, visceral fat, low physical activity, waist-to-hip ratio, and muscle mass index are key factors contributing to diabetes risk prediction. These findings highlight the potential of interpretable machine learning models for early detection of diabetes in low- and middle-income contexts, including Ecuador.