A hybrid deep learning and large language model framework for MACE risk prediction and evidence-based clinical recommendations.

Background: Major adverse cardiovascular events (MACE) remain a leading cause of global morbidity and mortality, necessitating accurate risk prediction and actionable prevention strategies. This study proposes a two-fold framework integrating deep learning and large language models (LLMs) to predict MACE risk and generate personalized, guideline-based recommendations. Methods: Emergency medical record (EMR) data from Chungbuk National University (CBNU) Hospital were preprocessed and split into training and test sets, with class imbalance addressed using SMOTETomek. A one-dimensional convolutional neural network (1D-CNN) was developed to predict individual MACE risk from clinical features. To translate predictions into practice, a retrieval-augmented generation (RAG) pipeline with prompt engineering was implemented using LLM Model (MISTRAL AI), grounded in WHO and AHA cardiovascular prevention guidelines. Predictive performance was evaluated using accuracy, precision, recall, and F1-score. The recommendation system was assessed using a rubric-based LLM judge evaluating guideline fidelity, clinical safety, dietary plans, lifestyle/exercise guidance, and OTC medication caution. Results: The proposed1D-CNN achieved strong performance, with overall accuracy of 0.97. For the MACE class, precision and recall reached 1.00, with an F1-score of 0.98. The recommendation system demonstrated high guideline fidelity (5/5 for both WHO and AHA), strong dietary and lifestyle guidance (5/5), good clinical safety (4/5), and moderate OTC medication caution (3/5). Conclusion: The proposed framework effectively combines accurate MACE risk prediction with interpretable, evidence-based preventive recommendations delivered via a web interface. This integrated approach supports clinical decision-making and personalized cardiovascular risk management.