A Digital Twin-Inspired Hybrid Variational Quantum Reinforcement Learning Framework for Heart Disease Risk Prediction
DOI:
https://doi.org/10.21015/vtcs.v14i1.2333Abstract
Despite many advancements in the medicine domain heart disease mortality rates continue climb that is signailing an urgent need for more advanced risk models. In this work we have tackled this challenge by introducing a hybrid framework that combines digital twin modeling with variational quantum reinforcement learning(VQRL). By developing patient-specific digital twins from raw patient clinical data, we are able to simulate unique health states within a specilized reinforcement learning environment. To optimize the diganostic decision-making we leveraged a variational quantum network. This setup utilizes a hybrid quantum-classical optimization loop, making it well suited for the constraints of modern near-term quantum hardware. When put to the test on the "Cleveland Heart Disease dataset" the proposed framework delivered impressive results: achieving a 94.2% accuracy, and an MCC of 0.82. Beyond just high scores in precision 90.4%, and recall 92.2%. Our framework demonstrated remarkable stability and an ability to generalize during the training phase. Ultimately, this fusion of digital twins and quantum computing offers a fresh, scalable path forward for clinical decision making.
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