Classical neural networks (NNs) have shown strong performance in medical data analysis. However, they typically require large labeled datasets and may struggle in data-scarce scenarios, common in clinical practice. Quantum Neural Networks (QNNs) have emerged as a promising alternative. This paper presents a comparative study between NNs and QNNs for heart disease prediction, addressing the limitations of current models in low-data regimes. We systematically evaluate 460 QNNs (using 11-13 qubits) and 4,480 NN architectures, analyzing key design parameters: encoding schemes, re-uploading strategies, circuit depth, and dropout (for QNNs), as well as hidden layers, neurons per layer, and dropout (for classical NNs). Top-performing models are selected for a direct comparison in terms of accuracy and sample complexity. Our results show QNNs achieve comparable accuracy and demonstrate potential advantages in data-scarce settings. Our study presents a structured and reproducible methodology for evaluating QNNs in clinical contexts, thereby supporting the broader investigation of quantum machine learning in applied healthcare domains.
Classical neural networks (NNs) have shown strong performance in medical data analysis. However, they typically require large labeled datasets and may struggle in data-scarce scenarios, common in clinical practice. Quantum Neural Networks (QNNs) have emerged as a promising alternative. This paper presents a comparative study between NNs and QNNs for heart disease prediction, addressing the limitations of current models in low-data regimes. We systematically evaluate 460 QNNs (using 11-13 qubits) and 4,480 NN architectures, analyzing key design parameters: encoding schemes, re-uploading strategies, circuit depth, and dropout (for QNNs), as well as hidden layers, neurons per layer, and dropout (for classical NNs). Top-performing models are selected for a direct comparison in terms of accuracy and sample complexity. Our results show QNNs achieve comparable accuracy and demonstrate potential advantages in data-scarce settings. Our study presents a structured and reproducible methodology for evaluating QNNs in clinical contexts, thereby supporting the broader investigation of quantum machine learning in applied healthcare domains.
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.
Quantum Machine Learning represents a promising approach to address complex computational challenges beyond classical computing capabilities. In this study, we investigate its application to healthcare, focusing specifically on the classification of cardiovascular diseases. Classical Machine Learning methods often struggle with high-dimensional data, resulting in limited accuracy and reduced generalization, especially in real-world medical scenarios. To overcome these issues, we propose a Hybrid Quantum-Neural Network that combines classical Autoencoders for dimensionality reduction with Quantum Neural Networks, facilitating effective data processing compatible with current NISQ hardware. Evaluated on the Cleveland dataset, our HQNN achieves state-of-the-art classification accuracy up to 90.98%, outperforming purely classical counterparts by capturing complex feature relationships in the quantum domain. We further demonstrate the effectiveness of the proposed HQNN model in scenarios characterized by limited training data and realistic noisy quantum environments, performing robustness analyses and simulations using IBM’s quantum hardware framework. These findings highlight the practical advantages of our Hybrid Quantum-Neural Network, underscoring its suitability and immediate applicability for real-world healthcare scenarios. The code is made publicly available.
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.
Effective and accurate diagnosis of diseases such as cancer, diabetes, and heart failure is crucial for timely medical intervention and improving patient survival rates. Machine learning has revolutionized diagnostic methods in recent years by developing classification models that detect diseases based on selected features. However, these classification tasks are often highly imbalanced, limiting the performance of classical models. Quantum models offer a promising alternative, exploiting their ability to express complex patterns by operating in a higher-dimensional computational space through superposition and entanglement. These unique properties make quantum models potentially more effective in addressing the challenges of imbalanced datasets. This work evaluates the potential of quantum classifiers in healthcare, focusing on Quantum Neural Networks (QNNs) and Quantum Support Vector Machines (QSVMs), comparing them with popular classical models. The study is based on three well-known healthcare datasets—Prostate Cancer, Heart Failure, and Diabetes.The results indicate that QSVMs outperform QNNs across all datasets due to their susceptibility to overfitting. Furthermore, quantum models prove the ability to overcome classical models in scenarios with high dataset imbalance. Although preliminary, these findings highlight the potential of quantum models in healthcare classification tasks and lead the way for further research in this domain.
The rapid advancements in artificial intelligence (AI) and quantum computing have catalyzed an unprecedented shift in the methodologies utilized for healthcare diagnostics and treatment optimization. This research paper explores the integration of quantum neural networks (QNNs) with classical machine learning (ML) algorithms to enhance diagnostic accuracy, facilitate personalized treatment plans, and predict patient outcomes with a higher degree of precision. Quantum neural networks, leveraging the principles of quantum mechanics such as superposition, entanglement, and quantum parallelism, have demonstrated the potential to perform complex computations more efficiently than classical counterparts. When coupled with established machine learning algorithms, QNNs can overcome traditional limitations in data processing, enabling more sophisticated models capable of uncovering intricate patterns in large and high-dimensional datasets. Machine learning, with its vast applications in the medical field, has long been instrumental in improving diagnostics and tailoring treatment regimens to patient-specific characteristics. However, despite significant advancements, classical ML approaches face substantial challenges, particularly in terms of computational complexity and the ability to process large-scale, multi-modal healthcare data effectively. Quantum neural networks address these challenges by introducing quantum computational paradigms that facilitate exponentially faster processing, allowing for real-time analysis of vast and complex datasets. The synergy between QNNs and ML algorithms introduces novel approaches that are poised to revolutionize predictive analytics in healthcare, optimizing patient outcomes and enabling highly personalized treatment plans. A key aspect of integrating quantum neural networks into machine learning frameworks is the potential for improved precision in diagnostic systems. Traditional diagnostic procedures often rely on predefined models that may overlook nuanced correlations within patient data. Quantum neural networks, with their ability to represent and process data in a quantum space, provide a more robust framework that can adaptively learn from intricate relationships in patient information. For instance, QNNs can significantly enhance the efficacy of disease detection algorithms, such as those used for identifying early-stage cancers or predicting the onset of chronic conditions like diabetes and heart disease, by offering superior pattern recognition capabilities. Furthermore, QNNs combined with classical machine learning architectures facilitate the creation of hybrid models that harness the strengths of both approaches, leading to diagnostic tools that are not only more precise but also more adaptive to varied data sources. The integration of QNNs with machine learning extends beyond diagnostics to personalized treatment optimization. Traditional treatment planning methodologies, including rule-based and data-driven ML models, often face difficulties in accounting for the multifaceted nature of patient data and individual variability. Quantum neural networks enhance this process by leveraging quantum algorithms that provide an efficient search space for complex treatment optimization problems, allowing for a more detailed understanding of patient responses and potential treatment outcomes. The ability of QNNs to perform parallel processing enables the assessment of a wide range of treatment scenarios simultaneously, leading to more accurate predictions regarding patient reactions to specific drugs, therapies, or medical interventions. This facilitates an adaptive approach that can recommend personalized treatment regimens based on comprehensive patient profiles, ultimately enhancing patient outcomes and reducing the likelihood of adverse drug reactions. In addition to enhancing diagnostics and treatment recommendations, quantum neural networks show promise in forecasting patient outcomes by offering a more robust analysis of longitudinal patient data. Forecasting models that leverage the combined power of quantum and classical algorithms can process historical data more rapidly, allowing healthcare providers to anticipate potential health issues and intervene earlier. For example, predictive models utilizing QNNs can anticipate patient deterioration in critical care settings, facilitating timely interventions that mitigate risks and improve survival rates. Such predictive models can be instrumental in managing chronic diseases, monitoring recovery trajectories, and optimizing resource allocation within healthcare systems, thus contributing to overall efficiency and better resource management. Despite the promising capabilities of integrating QNNs with ML algorithms, there are notable challenges that need to be addressed to fully realize their potential. The practical implementation of quantum algorithms in a healthcare context faces hurdles related to hardware limitations, the need for high fidelity in quantum states, and the scalability of quantum systems to handle real-world clinical data. Additionally, the hybrid nature of combining classical and quantum approaches requires sophisticated algorithms that can bridge the gap between quantum computation and classical data processing pipelines. Solutions to these challenges may include advancements in quantum hardware, such as the development of more stable qubits and noise reduction techniques, as well as the optimization of hybrid algorithms that leverage both classical machine learning and quantum computing capabilities effectively. The exploration of quantum neural networks for healthcare applications also necessitates rigorous ethical considerations, particularly in ensuring data privacy and security. The incorporation of quantum computing must comply with healthcare data protection regulations, and quantum algorithms must be designed to maintain patient confidentiality while processing sensitive health data. Moreover, the interpretability of quantum models poses challenges that could hinder their acceptance in clinical practice. Advances in explainable AI and quantum algorithm transparency are crucial to foster trust among healthcare professionals and patients alike. Integration of quantum neural networks with classical machine learning models represents a transformative approach that could significantly advance healthcare diagnostics, personalized treatment strategies, and patient outcome prediction. By harnessing the computational advantages offered by quantum systems and the flexibility of machine learning algorithms, healthcare applications can achieve a new level of precision and adaptability. Despite current challenges, continued research into quantum algorithms, quantum hardware development, and hybrid computational models promises substantial strides in overcoming these limitations. The synergy between QNNs and ML algorithms could ultimately lead to more effective, personalized, and efficient healthcare solutions, ushering in a new era of data-driven medical care characterized by increased diagnostic accuracy and improved treatment outcomes. As the field evolves, interdisciplinary collaboration between quantum physicists, computer scientists, and healthcare professionals will be vital to unlock the full potential of these innovative computational techniques and bring them to mainstream clinical use.
The aim of this study is to enhance the accuracy and reduce the time complexity of predicting cardiac illnesses by utilizing patient vital signs exclusively. The importance of timely treatment in critical situations where quick decisions are necessary is emphasized here. Precise predictions have the potential to prevent health deterioration and even save lives. By analyzing vast datasets (images, texts etc.) and detecting subtle patterns, quantum machine learning algorithms (QML) can offer more accurate results in a shorter period compared to classical algorithms. The feature set used here is a novel one to be used for the quick detection and early prediction of cardiovascular diseases, and also a convenient one for this task. To predict heart diseases or abnormalities, both classical and quantum machine learning techniques have been employed in this paper. We have developed four models based on Support Vector Machine (SVM), Neural Network (NN), Quantum Support Vector Machine (QSVM), and Quantum Neural Network (QNN) and compared their performance on a balanced sample of a vast dataset for heart diseases prediction. After comparing the models' performance, we found that Quantum Support Vector Machine (QSVM) performed best with an accuracy of 75% and to-fold Cross-validation score of 80%.