Personalized AI Cardiovascular Risk, Twin Using Explainable Reinforcement Learning

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• Semantic Scholar2026-07-16 23:31:51
  Personalized AI Cardiovascular Risk, Twin Using Explainable Reinforcement Learning

  Cardiovasculardiseases (CVDs) remain one of the leading causes of mortality worldwide, emphasizing the need for intelligent systems capable of predicting disease progression and recommending personalized interventions. Conventional cardiovascular risk assessment models, including Framingham Risk Score and ASCVD calculators, provide static predictions and fail to capture the dynamic evolution of patient health over time. This research presents a Personalized AI Cardiovascular Risk Twin that integrates machine learning, deep learning, reinforcement learning, and explainable artificial intelligence into a unified clinical decision-support framework. A soft-voting ensemble classifier combining Logistic Regression and XGBoost is employed for accurate cardiovascular risk prediction. A GRU-Attention-based Digital Twin models temporal physiological changes and forecasts future cardiovascular trajectories. Based on this digital twin environment, a Proximal Policy Optimization (PPO) reinforcement learning agent learns personalized lifestyle and therapeutic interventions while maximizing long-term health outcomes. To ensure patient safety, a Rule-Guided Clinical Action-Masking Engine eliminates clinically inappropriate recommendations before policy execution. Model interpretability is enhanced using Kernel SHAP, enabling clinicians to understand feature contributions for every prediction. The complete framework is deployed through FastAPI, Streamlit, and SQLite to provide real-time prediction, simulation, explanation, and audit capabilities. Experimental evaluation demonstrates that the proposed framework improves predictive performance, supports personalized treatment planning, enhances transparency, and promotes clinically reliable AI-assisted cardiovascular care.