The increasing global prevalence of cardiovascular disorders demands intelligent healthcare architectures that transcend traditional statistical prediction models, evolving into autonomous clinical systems capable of perceiving complex patient data, reasoning over multidimensional biomedical relationships, and actuating real-time diagnostic decisions. This paper introduces Graph Attention Networks with Symbolic Reinforcement Learning (GAT-SRL), a novel AI-driven framework tailored for heart disease prediction, embedding advanced artificial intelligence to construct adaptive clinical decision-support systems that self- optimize across diagnostic, analytical, and medical reasoning layers.

GAT-SRL synergizes Graph Attention Networks (GATv2) for relational modeling of patient-risk interactions, high-dimensional feature embeddings for capturing nonlinear dependencies among cardiovascular attributes, and symbolic reinforcement learning for verifiable and guideline-aligned decision-making. Unlike conventional machine learning approaches—such as Logistic Regression, Random Forest, Support Vector Machines, and deep neural networks  that operate as opaque black-box classifiers— GAT-SRL achieves           end-to-end interpretability through symbolic diagnostic traces, adaptive risk recalibration, and attention-driven feature prioritization.