Quantum Computing Method for Prediction of the Cardiovascular Disease

Cardiovascular complications pertain to damages done to the myocardium and the arteries, as well as other parts of the circulatory system. Some important ischemic heart disease (IHD) risk factors include some health assessment metrics, age, and gender. In the prediction of cardiac diseases and in other tough decisions involving the analytics of large volumes of medical data, quantum computation techniques have become a staple in the healthcare field. Studies recently focus on the application of such procedures in quantum learning (QL) algorithms. For heart disease diagnosis, this work came up with a set of QL algorithms, including optimized quantum qubit vector learning (OQQVL) and quantum stacked convolutional neural networks (QSCNN), which we consider computationally light. By incorporating strong quantum learning based pre‐processing and feature clustering techniques like quantum K means, the proposed models' prediction reliability along with accuracy is sharpened whilst ensuring robustness. A trade‐off between the proposed and more sophisticated recently published models is done using available performance metrics. Thus, using the proposed techniques, the models achieved the highest accuracies of 99.1% and 99.5%, respectively. Moreover, the sophisticated structure provided reasonable execution times, making them suitable for real‐time healthcare use.