This study proposes a lightweight hybrid model that integrates probabilistic feature augmentation, Gated Recurrent Units (GRU), and Multi-Head Attention to enhance chronic disease prediction on imbalanced clinical tabular data. The research addresses the challenge of low recall and poor minority-class detection in conventional models, aiming to improve predictive robustness and interpretability. The proposed model leverages logistic regression to generate probability-based feature augmentations, which are combined with sequential dependencies learned by GRU and refined through attention mechanisms. Evaluated on three benchmark medical datasets, Breast Cancer, Heart Disease, and Hepatitis C, the model achieves a maximum F1-score of 0.951, a recall of 0.944, and an AUC of 0.976, outperforming traditional machine learning baselines and single-path deep learning models. The attention module enhances interpretability by highlighting relevant features, supporting clinical insights. These findings confirm that probabilistic augmentation and attention-guided deep architectures can significantly improve prediction performance on imbalanced medical data. The results support the study’s objective to design an accurate, interpretable, and clinically relevant prediction model.