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.
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.
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.
The cardiovascular and neurological conditions are one of the predominant causes of death and disability at a global scale, thus the need to develop smart healthcare systems to detect diseases at their initial stages or at all. The limitation of the conventional artificial intelligence-based and machine learning-based healthcare models are usually rife with high levels of computation complexity, low levels of prediction capabilities, poor scaling, and ineffective processing of complex healthcare data. The proposed solution to these problems is a Quantum Neural Network-Based Healthcare Analytics Framework that could be used to detect cardiovascular and neurological disorders at their initial stages. This framework is a combination of quantum computing and neural network models to improve diagnostic abilities in the field of healthcare analytics in disease classification, predictive accuracy, and computational efficiency. Experimental evaluation was done using healthcare datasets that comprised cardiovascular and neurological records of patients after being processed through preprocessing methods such as normalization, feature extraction and data balancing. Classification performance measures were used to assess the performance of the proposed model and these measures include Accuracy, Precision, Recall, Specificity, F1-Score, and AUC-ROC. The experimental findings showed that the suggested quantum neural network model worked much better than the traditional machine learning and deep learning models with regard to disease prediction accuracy, classification reliability, and the ability to detect disease early. Additionally, the framework had better sensitivity and lower false prediction, thus improving clinical decision-making support. The study helps expand the intelligent quantum healthcare analytics system to the next generation of medical diagnosis and predictive healthcare uses.
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.
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.
Prompt diagnosis of disease is a strong driver for decreasing mortality, improving patient care and healthcare expenditures. Classical machine learning (ML), which has shown great potential in bolstering predictive modeling throughout oncology, cardiology, and neurology, runs the risk of excessive data dimensionality along with high computational complexity. Quantum computing is an exciting new paradigm for computation that takes advantage of superposition and entanglement which can be beneficial in learning patterns in high-dimensional spaces and for kernel-based learning. This review provides a systematic summary of the preliminary work on quantum machine learning (QML) with empirical applications to early disease diagnosis, while emphasizing its theoretical foundations, algorithmic developments and application in healthcare. We scope the conversation on quantum support vector machines, variational quantum classifiers, quantum kernels and hybrid architectures for a quantum classical. We also examine current hardware limitations in the NISQ era and assess how close these methods are to being ready for translation into public health applications. Although widespread clinical application is still far away, hybrid QML approaches are finding utility in low-data and high-complexity settings. We conclude with a discussion of research challenges, regulation and strategy towards realizing such quantum-enhanced precision diagnostics through U.S. public health innovation initiatives
At the intersection of quantum computing and machine learning, this review paper explores the transformative impact these technologies are having on the capabilities of data processing and analysis, far surpassing the bounds of traditional computational methods. Drawing upon an in-depth analysis of 32 seminal papers, this review delves into the interplay between quantum computing and machine learning, focusing on transcending the limitations of classical computing in advanced data processing and applications. This review emphasizes the potential of quantum-enhanced methods in enhancing cybersecurity, a critical sector that stands to benefit significantly from these advancements. The literature review, primarily leveraging Science Direct as an academic database, delves into the transformative effects of quantum technologies on machine learning, drawing insights from a diverse collection of studies and scholarly articles. While the focus is primarily on the growing significance of quantum computing in cybersecurity, the review also acknowledges the promising implications for other sectors as the field matures. Our systematic approach categorizes sources based on quantum machine learning algorithms, applications, challenges, and potential future developments, uncovering that quantum computing is increasingly being implemented in practical machine learning scenarios. The review highlights advancements in quantum-enhanced machine learning algorithms and their potential applications in sectors such as cybersecurity, emphasizing the need for industry-specific solutions while considering ethical and security concerns. By presenting an overview of the current state and projecting future directions, the paper sets a foundation for ongoing research and strategic advancement in quantum machine learning.