Quantum Machine Learning Applied to Electronic Healthcare Records for Ischemic Heart Disease Classification

Cardiovascular diseases refer to diseases that affect the heart and blood arteries. Most strategies developed to predict ischemic heart disease (IHD) are focused on pain characteristics, age, and sex, but many variables have been described as determinant risk factors for developing IHD. Therefore, machine learning algorithms are essential to make efficient decisions in predicting cardiac disease in the healthcare industry by considering a lot of medical data. Recent research has focused on implementing these approaches to quantum machine learning (QML) algorithms. This research proposes a set of computationally efficient QML algorithms, optimized quantum support vector machine (OQSVM), and hybrid quantum multi-layer perceptron (HQMLP) for the classification of cardiovascular disease. The use of efficient pre-processing and the robust feature selection techniques, i.e., Wrapper and Filter method improves the prediction rate and ensures the robustness of the proposed models. All the models are evaluated using the real-time cardiovascular dataset and recorded the performance in terms of accuracy. The performance metrics of the proposed models are compared to those of recently published models with more complicated architectures. The highest accuracies of the proposed OQSVM, and HQMLP models, considering 10 features of the cardiovascular dataset, are recorded at 94% and 93%, respectively. Furthermore, the proposed models are computationally effective and can be preferred for real-time healthcare applications.