19 Quantum computing-assisted machine learning to improve the prediction of cardiovascular disease in healthcare system was published in Quantum-Safe Cryptography Algorithms and Approaches on page 265.
This paper aims to explore the emerging field and future directions of quantum machine learning for improving the detection of cardiovascular diseases through a literature review. Multiple academic and professional databases such as PubMed, IEEE Xplore, Google Scholar, Web of Science, arXiv and ProQuest are used to retrieve the relevant articles. The underlying structure, current applications, and potential advantages of quantum machine learning over traditional machine learning approaches in healthcare are then discussed. The study shows that even though the application of quantum machine learning is still in its early stages, it will likely advance as more research is done leading to even more impressive and potential applications. For future developments, the paper seeks to provide a clear understanding of the detection and diagnosis of cardiovascular diseases with the help of quantum machine learning, leading to more effective and personalised healthcare solutions.
Cardiovascular disease is one of the most common reasons of increases the global death rate, suggesting initial detection and timely treatment crucial. While traditional machine learning models have been effective in predicting heart disease, Quantum Computing (QC) introduces new possibilities for improving accuracy and processing speed. In this regard the application of Quantum Machine Learning (QML) approaches-categorically, Quantum Neural Networks (QNN) and Quantum Support Vector Classification (QSVC)-to predict heart disease using key health indicators such as present-age, levels of cholesterol, Blood Pressure (BP), and lifestyle choices. QNN is utilized to recognize complex patterns in patient data, leveraging deep learning to understand intricate connections between various risk factors. Meanwhile, QSVC takes advantage of QC ability to compute large amounts of information at once, making classification faster and more precise. Both models are trained and tested on a heart disease dataset and compared with traditional machine learning methods to assess their accuracy, efficiency, and scalability. The results show that while quantum and classical models achieve similar accuracy, quantum computing significantly reduces processing time, making it highly efficient for analyzing large datasets. This study highlights the strength of QC in transforming medical diagnostics, offering faster and more reliable predictive models for early heart disease detection. By integrating quantum algorithms into healthcare, this research contributes to the advancement of AI-driven medical solutions, resulting in more efficient and individual patient care.
Cardiac risk prediction is critical for the early detection and prevention of cardiovascular diseases, a leading global cause of mortality. In response to the growing volume and complexity of healthcare data, there has been increasing reliance on computational approaches to enhance clinical decision-making and improve early detection of cardiac risks. Although classical machine learning techniques have demonstrated strong performance in cardiovascular disease prediction, their efficiency and scalability are increasingly challenged by high-dimensional and large-scale medical datasets. Emerging advances in quantum computing have introduced quantum machine learning (QML) as a promising alternative, offering novel computational paradigms with the potential to outperform classical methods in terms of speed and problem-solving capability. This review analyzed twelve studies, evaluating data types, quantum architecture, performance metrics, and comparative efficacy against classical machine learning models. Our findings indicate that QNNs show promise for enhanced predictive accuracy and computational efficiency. However, significant challenges in scalability, noise resilience, and clinical integration persist. The translation of quantum advantage into clinical practice necessitates further validation on large-scale with diverse datasets.
ABSTRACT 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.
Quantum computing has emerged as a powerful tool for solving complex problems in various fields. Personalized medicine, tailoring medical treatment to patients based on their genetic and health data, is one area where predictive analytics can be useful. This work explores the application of quantum algorithms for predictive analytics, specifically in the context of predicting outcomes of cardiovascular disease based on patient data. This research is focused on the quantum-based predictive models for the case study of cardiovascular disease. The models are based on Quantum Support Vector Machines, Quantum Neural Networks, and Variational Quantum Eigensolver algorithms. The software implementation is based on the Python programming language, including an integrated quantum algorithm. A dataset of cardiovascular disease from an online platform is used to train and evaluate the models.
Health care is playing a major role in designing any kind of prediction model and there is a large amount of data related to different diseases is available over the globe. A major part of machine learning lies in the identification of the data related to different diseases and make them helpfulfor people. The major implementation is in the identification ofthe safest medical data systems if the new challenging task where we can implement quantum computing as the main lead to process the medical data and making the new data systems which are most secured to manage the medical data which is useful to identify the fastest method to form a drug and also to manage in the identification of the safe drug mechanism for thedisease which is not even in identification. Quantum computingis a challenging area that is like an extension to the current scenario of medical imaging. Improved healthcare system using machine learning and quantum computing gives a new age to medical sciences to identify the approximate solutions to the different medical diseases. This article works on the implementation of such medical advancements to a specificdisorder based on human genetics and DNA sequencing. The Quantum computing approach succeeded in recognizing the approximate medical sequences for a particular disease.