Probabilities Feature Enrichment for Improved Early Diabetes Prediction

Early prediction of diabetes remains a major healthcare challenge, as timely diagnosis can significantly reduce the risk of severe complications. Most existing studies emphasize complex predictive models or extensive preprocessing pipelines, while comparatively limited attention has been given to improving feature representations. In this study, we propose a statistical framework for enriching the feature space of diabetes data using Gaussian Mixture Models (GMMs) and Kernel Density Estimation (KDE) to capture latent distributional characteristics and local density patterns. The resulting enriched features enhance class discrimination while preserving model simplicity, allowing standard classifiers to operate more effectively. The proposed framework is evaluated on two diabetes datasets with different sample sizes and class distributions. Models trained on the enriched feature set are compared with baseline models trained solely on the original features, using multiple strategies, including direct feature concatenation and ensemble-based approaches. Experimental results show that the enriched feature space consistently outperforms the original feature set, leading to notable improvements in overall predictive performance. In particular, the framework achieves a substantial increase in sensitivity for the minority class (diabetic cases), which is especially important for clinical applications where positive samples are typically scarce relative to healthy cases.