Coronary heart disease (CHD) is a severe cardiac disease, and hence, its early diagnosis is essential as it improves treatment results and saves money on medical care. The prevailing development of quantum computing and machine learning (ML) technologies may bring practical improvement to the performance of CHD diagnosis. Quantum machine learning (QML) is receiving tremendous interest in various disciplines due to its higher performance and capabilities. A quantum leap in the healthcare industry will increase processing power and optimise multiple models. Techniques for QML have the potential to forecast cardiac disease and help in early detection. To predict the risk of coronary heart disease, a hybrid approach utilizing an ensemble machine learning model based on QML classifiers is presented in this paper. Our approach, with its unique ability to address multidimensional healthcare data, reassures the method's robustness by fusing quantum and classical ML algorithms in a multi-step inferential framework. The marked rise in heart disease and death rates impacts worldwide human health and the global economy. Reducing cardiac morbidity and mortality requires early detection of heart disease. In this research, a hybrid approach utilizes techniques with quantum computing capabilities to tackle complex problems that are not amenable to conventional machine learning algorithms and to minimize computational expenses. The proposed method has been developed in the Raspberry Pi 5 Graphics Processing Unit (GPU) platform and tested on a broad dataset that integrates clinical and imaging data from patients suffering from CHD and healthy controls. Compared to classical machine learning models, the accuracy, sensitivity, F1 score, and specificity of the proposed hybrid QML model used with CHD are manifold higher.

翻译：冠心病是一种严重的心脏疾病，其早期诊断至关重要，因为它能改善治疗效果并节省医疗费用。量子计算与机器学习技术的快速发展有望为冠心病诊断性能带来实质性提升。量子机器学习凭借其更高的性能与能力，正受到各领域的广泛关注。医疗保健行业的量子飞跃将提升处理能力并优化多种模型。量子机器学习技术具备预测心脏疾病并辅助早期检测的潜力。本文提出一种基于量子机器学习分类器的集成机器学习混合方法，用于预测冠心病风险。该方法以其处理多维医疗数据的独特能力，通过在多步推理框架中融合量子与经典机器学习算法，确保了方法的鲁棒性。心脏病发病率与死亡率的显著上升正影响着全球人类健康与经济。降低心脏疾病发病率和死亡率需要早期检测。本研究采用具备量子计算能力的混合方法，以解决传统机器学习算法难以处理的复杂问题，并降低计算成本。所提方法已在树莓派5图形处理器平台上开发，并在整合了冠心病患者与健康对照组临床及影像学数据的大规模数据集上进行了测试。与经典机器学习模型相比，所提出的混合量子机器学习模型在冠心病应用中展现的准确率、灵敏度、F1分数及特异度均显著更高。