Predicting Cardiovascular Disease Using a Quantum Support Vector Machine

Cardiovascular disease (CVD) is the leading cause of global mortality, with over 19.8 million deaths recorded in 2022, underscoring the urgent necessity for precise early-stage computational diagnostic techniques. The traditional machine learning approaches tend to fail in the nonlinear feature interactions of high-dimensional clinical data. This paper provides a Quantum Support Vector Machine (QSVM) framework that uses a fidelity-based quantum kernel, calculated using PennyLane Lightning, and a qubit simulator to map patient data into a high-dimensional Hilbert space, enabling complex feature interactions that are unattainable with traditional kernels. The model was tested on a publicly available UCI-based heart disease dataset that included 1,025 patient records and 13 clinical features, such as chest pain type, ST depression, maximum heart rate, and thalassemia test results, with a near-balanced distribution between the classes (51.3% positive, 48.7% negative). All 13 features were represented in quantum states with a Hadamard-RY feature map applied to 13 qubits, and quantum pipelining was used to perform batch executions of the feature map and reduce simulation overhead. The QSVM achieved 93.90% accuracy, outperforming the classical SVM baseline, which achieved 92.50% on the same dataset. Complexity analysis shows O(d) depth, linear qubit scaling, and NISQ-compatible scalable quantum biomedical diagnostics.