Predicting Individuals at Risk of Diabetes Using Machine Learning

Diabetes mellitus (DM) is a serious global health problem due to the large number of people who suffer from it, the complications arising from it, and the significant economic impact associated with it. Early identification of people at risk is essential for implementing preventive strategies that reduce the burden on healthcare systems. In this context, machine learning (ML) techniques have emerged as promising tools to support the timely detection of diabetes and clinical decision-making. The objective of the research was to develop and evaluate the performance of ML models to predict diabetes risk using clinical and sociodemographic data obtained at a primary care clinic of the Instituto Mexicano del Seguro Social (IMSS) in Saltillo, Coahuila, Mexico, the main healthcare system in Mexico. Data from 1,903 patients were analyzed, taking into account demographic and clinical variables and health habits. The database is not balanced; there are more healthy people than sick people. Supervised algorithms such as support vector machine (SVM), logistic regression (LR), random forest (RF), multilayer perceptron (MLP), and k-nearest neighbors (KNN) were implemented. In addition, to improve performance, ensemble techniques and class balancing methods, such as the Synthetic Minority Oversampling Technique (SMOTE), were applied. Performance was evaluated using metrics such as sensitivity and specificity, among others. The results show that the models predict people without diabetes risk (class 0) with a high percentage, with sensitivities greater than 90% in several algorithms. In contrast, for detecting people at risk (class 1), the percentages are low, with sensitivities between 25% and 40% in the base models. The application of SMOTE and ensemble techniques, such as Extreme Gradient Boosting (XGBoost), increased sensitivity to 79%, although with an associated reduction in specificity (51.9%). These results show that, although ML models have remarkable potential to support the identification of diabetes risk, class imbalance remains a critical challenge. Improving the sensitivity of these tools is essential to promote confirmatory studies, facilitate early treatment initiation, and reduce the onset of chronic complications. In addition, interpretable and clinically verifiable models can strengthen doctor-patient communication, encourage self-care, and promote the early adoption of preventive interventions aligned with international public health recommendations.