<![CDATA[Cardiovascular disease remains the leading cause of death globally, demanding accurate early detection, yet limited access to specialist medical personnel in developing countries often hinders timely diagnosis. This study aims to address the critical gap between the high accuracy of machine learning models in academic research and the minimal adoption of practical clinical applications by developing a safe and trustworthy hybrid artificial intelligence-based heart disease triage system. The proposed methodology integrates a dual-model architecture in which Support Vector Machine serves as the primary prediction model and Extreme Gradient Boosting as a second-opinion model, both optimized with SMOTE oversampling technique to handle class imbalance, and implements SHAP to provide transparency in black-box model decisions. The system is enriched with Dynamic Prompt Engineering innovation on the Mistral-7B Large Language Model to translate numerical probabilities into safe, personalized, and empathetic medical narratives. Experimental results show that the Support Vector Machine model with RBF kernel delivers superior performance with an accuracy of 90.22% and sensitivity of 94.12%, which is crucial for minimizing false negative cases in medical screening, outperforming the Extreme Gradient Boosting model which recorded 88.04% accuracy. Interpretability analysis identified chest pain type, cholesterol level, and maximum heart rate as the primary risk indicators, validating the model's alignment with standard cardiology guidelines. A dual safety validation mechanism through programmed risk thresholds and language generation temperature control ensures the system does not produce harmful diagnostic hallucinations. In conclusion, the system implemented as a FastAPI-based microservice is proven technically feasible with low latency, offering an accurate, transparent, and communicative early screening solution to support healthcare service efficiency.]]>
