Prediction of Cardiac Disease Using Quantum Enhanced Ensemble Learning Approach

Our research introduces a state-of-the-art bagging ensemble model that leverages Quantum Support Vector Classifiers (QSVC) to assess the risk of heart disease. Testing involved rigorous experimentation on the Cleveland data set for which QSVC has demonstrated better performance compared with benchmark classifiers and other quantum-inspired models as well, achieving an excellent accuracy of 90.16%. The boosted accuracy shows the competence of quantum machine learning in serving as a potential upgrade of predictive analytics within the healthcare industry. In addition to its impressive performance metrics, the model incorporates SHAP (SHapley Additive exPlanations) analysis, which significantly enhances its interpretability. By identifying and elucidating the utmost need of various features in this entire route, SHAP analysis makes the persons who are into medical practicing get to know more about the core of this model. This transparency is crucial in medical applications, where understanding the factors influencing a diagnosis can lead to better key decisions and sentiments of patients. Thus, Implications of this research extend beyond mere performance statistics. The research shows that quantum machine learning, and specifically QSVC, can revolutionize healthcare by providing interpretable and accurate predictive models. Over time, the technology will keep improving, and ultimately it will improve patient care and the quality of healthcare.