Cardio Twin: A Quantum-Optimized 3D Digital Twin for Personalized Heart Health Simulation and Prevention

Author

Y. Devid Rex, P. Himasahithi, M. Dhanunjay, A. Tarunkumar, Dr. Sreejyothsna Ankam

Abstract

 Cardiovascular diseases (CVDs) remain one of the leading causes of mortality worldwide, highlighting the need for intelligent systems that support early risk assessment and personalized preventive care. Existing heart disease prediction models primarily focus patient risk using machine learning techniques, providing limited insight into. To address these limitations, this project proposes Cardio Twin Quantum, how changes in lifestyle or health conditions may influence future cardiac outcomes a novel framework that integrates a personalized 3D digital twin, quantum-enhanced optimization, artificial intelligence, and future health simulation into a unified decision-support system. The proposed system creates a virtual representation of an individual's heart using clinical and lifestyle parameters such as blood pressure, cholesterol, body mass index (BMI), physical activity, sleep quality, stress levels, and habits. Advanced machine learning models are employed to estimate cardiovascular risk, while explainable AI techniques provide transparent interpretations of the prediction results. Unlike conventional prediction systems, the digital twin dynamically visualizes the patient's current cardiac condition and simulates future health trajectories under different intervention scenarios, enabling users to explore the long-term impact of behavioral modifications. The integration of interactive 3D visualization, personalized health recommendations, explainable AI, and quantum optimization enhances user engagement and supports preventive healthcare decision-making. By combining emerging technologies within a single platform, CardioTwin presents an innovative approach toward intelligent cardiovascular health management and demonstrates the potential of digital twins and quantum computing in advancing personalized medicine.

Keywords

Cardiovascular Disease, Digital Twin, Quantum Computing, Quantum Optimization, Artificial Intelligence, Explainable AI, Heart Disease Prediction, Personalized Healthcare, 3D Visualization, Preventive Healthcare, Machine Learning.

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References

   This project offers Cardio Twin: a Quantum-Optimized 3D Digital Twin for heart health simulation and prevention. Cardio Twin brings together health data, artificial intelligence, classical machine learning, quantum machine learning, explainable AI, three‑dimensional visualization and what‑if health simulations. Cardio Twin aims to combine past information with current health parameters and predictive model outputs to support personalized cardiovascular risk assessment. I believe Cardio Twin can give patients a picture of their heart health.

Cardio Twin uses classical machine learning algorithms such as Random Forest and XGBoost for predicting heart disease risk. Cardio Twin also looks at machine learning techniques, including Quantum Support Vector Machine (QSVM) and Quantum Neural Network (QNN) to classify cardiovascular conditions. Cardio Twin applies AI with SHapley Additive exPlanations (SHAP) to identify which health features influence model predictions. Cardio Twin integrates these outputs into the twin representing historical, current predicted and simulated cardiovascular health states. Cardio Twin’s what‑if simulation module lets users explore how hypothetical changes in lifestyle parameters may influence predicted risk. You might find the visual interface helpful for understanding risk factors.

The main contribution of Cardio Twin is the integration of heart disease prediction, model explainability and personalized health simulation within a 3D cardiovascular digital twin. Of presenting prediction results only as numbers Cardio Twin offers a visual environment where users can explore health indicators understand contributing risk factors and compare hypothetical scenarios. I think this integrated approach can support health awareness and preventive planning.

Overall Cardio Twin provides a framework for combining AI, quantum machine learning, explainable AI and digital twin technology to support personalized risk prediction and health simulation.With experimental evaluation and clinical validation Cardio Twin could serve as a foundation, for future intelligent cardiovascular health monitoring and preventive decision‑support applications. I think this could change how patients and doctors approach heart health.

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