According to the World Health Organization, cardiovascular diseases are responsible for several million deaths worldwide each year, thus highlighting a prime necessity for developing methods of diagnosis that are accurate and rapid. Although recent advancements in quantum-enhanced machine learning have demonstrated enhanced prediction performance, current research is still limited by the reliance on static datasets, the lack of interpretability, and the lack of privacy-preserving mechanisms for data sharing across institutions. In this paper, we introduce an Explainable Quantum-Enhanced Federated Learning Framework for Real-Time Cardiovascular Risk Assessment that integrates the scalability of federated learning with the computational benefits of quantum models to allow multiple healthcare centers and remote devices to train models collaboratively without sharing raw patient data. Quantum-inspired classifiers such as variational circuits and Quantum SVM are integrated into the federated pipeline to accelerate convergence and improve robustness. An explanation layer was implemented to enhance clinical trust by applying changed feature attribution methods that help the physicians comprehend how much the important factors like cholesterol, blood pressure, and ECG patterns contribute to the risk predictions. In addition, the system is meant for constant watch by the mixing of electronic health records and wearable sensor streams, thus making it possible to switch from one-time prediction to dynamically and personally tailored risk assessment. The proposed technique has been validated through experiments on standard heart disease datasets where it is also said to have done the opposite: it has improved predictive accuracy, reduced communication overhead compared to traditional federated systems, and provided insights that are ready for supporting clinical decision-making in real practice. Consequently, this framework is a first step towards quantum-driven healthcare solutions that are safe, scalable, and explainable.
According to the World Health Organization, cardiovascular diseases are responsible for several million deaths worldwide each year, thus highlighting a prime necessity for developing methods of diagnosis that are accurate and rapid. Although recent advancements in quantum-enhanced machine learning have demonstrated enhanced prediction performance, current research is still limited by the reliance on static datasets, the lack of interpretability, and the lack of privacy-preserving mechanisms for data sharing across institutions. In this paper, we introduce an Explainable Quantum-Enhanced Federated Learning Framework for Real-Time Cardiovascular Risk Assessment that integrates the scalability of federated learning with the computational benefits of quantum models to allow multiple healthcare centers and remote devices to train models collaboratively without sharing raw patient data. Quantum-inspired classifiers such as variational circuits and Quantum SVM are integrated into the federated pipeline to accelerate convergence and improve robustness. An explanation layer was implemented to enhance clinical trust by applying changed feature attribution methods that help the physicians comprehend how much the important factors like cholesterol, blood pressure, and ECG patterns contribute to the risk predictions. In addition, the system is meant for constant watch by the mixing of electronic health records and wearable sensor streams, thus making it possible to switch from one-time prediction to dynamically and personally tailored risk assessment. The proposed technique has been validated through experiments on standard heart disease datasets where it is also said to have done the opposite: it has improved predictive accuracy, reduced communication overhead compared to traditional federated systems, and provided insights that are ready for supporting clinical decision-making in real practice. Consequently, this framework is a first step towards quantum-driven healthcare solutions that are safe, scalable, and explainable.
Modern healthcare systems increasingly rely on artificial intelligence, deep learning, and medical imaging technologies for accurate disease diagnosis and clinical decision-making. However, conventional AI systems still face challenges related to interpretability, computational complexity, centralized data dependency, and patient privacy. This paper presents an Explainable Quantum Deep Learning Framework integrating Vision Transformers, Federated Intelligence, and Explainable Artificial Intelligence techniques for smart healthcare applications. The proposed framework combines quantum-inspired optimization, multimodal medical data analysis, transformer-based feature extraction, and decentralized federated learning to improve diagnostic accuracy, scalability, privacy preservation, and interpretability. Explainability mechanisms including SHAP and Grad-CAM are incorporated to enhance physician trust and clinical transparency. Experimental evaluation demonstrates superior performance in biomedical image classification and disease prediction compared with conventional deep learning models, providing a scalable solution for next-generation intelligent healthcare systems.
Cardiovascular disease remains one of the leading causes of death worldwide, making early and accurate risk prediction essential. Conventional machine learning models often struggle to handle complex medical data, resulting in limited accuracy and reduced interpretability. To address these challenges, this study presents a hybrid prediction framework that combines Quantum Machine Learning (QML) with ensemble learning techniques. Classical models such as Support Vector Classifier (SVC) and Artificial Neural Network (ANN) are used as baseline approaches. In addition, quantum models including QSVC, Variational Quantum Classifier (VQC), and Quantum Neural Network (QNN) are employed to capture complex non-linear patterns present in clinical data. To further enhance performance, a Bagging-QSVC ensemble model is proposed, improving robustness and overall classification accuracy. Interpretability is incorporated using SHAP, allowing the model’s predictions to be more transparent and easier to understand in a clinical context. Experimental results indicate that the proposed quantum ensemble achieves an accuracy of around 90%, outperforming both classical models and individual quantum models. Furthermore, a real-time user interface is developed, enabling clinicians to input patient data and receive immediate risk predictions. Overall, the system provides a reliable and interpretable solution suitable for practical clinical decision support.
The growing occurrence of patients with multiple chronic diseases has increased the need for efficient multidisease risk prediction systems in healthcare. However, clinical data is often distributed in various cloud, but it lacks in efficient privacy preservation. To address these challenges, this project proposes a hybridized framework for privacy-aware multi-disease risk prediction in distributed healthcare environments. nA key challenge in multi-disease data collection from heterogeneous clinical data may lag in normalization. So, cleaning and preprocessing is done on the first phase. Second challenge lies in capturing complex and non-linear relationships among clinical features within reduced feature spaces. To handle this, quantum-inspired feature encoding and variational quantum circuits are employed to enhance feature representations. Since data is distributed across cloud data privacy and confidentiality issues exist. To overcome this issue Federated Learning for multi-disease prediction is applied in the third phase. To address these challenges, this paper proposes a novel framework titled Hybridized Quantum Computing and Federated Learning for Multi-Disease Prediction (HQCF-MDRP). The proposed system integrates structured data preprocessing, quantum-assisted feature representation, and federated collaborative learning into a unified architecture. Initially, healthcare datasets are cleaned, normalized, and subjected to feature selection to improve data quality and reduce dimensionality. Selected features are then transformed using quantum-inspired angle encoding and Variational Quantum Circuits (VQC), enabling enhanced representation of hidden correlations among clinical attributes. Subsequently, Federated Learning with FedProx optimization is employed to train predictive models collaboratively across distributed healthcare institutions without sharing raw patient data.
Disease hazard prediction has been transformed by the use of artificial intelligence in healthcare; however, there are still problems with striking a balance between interpretability and predictive routine, mostly when working with complex, high-dimensional clinical data. To improve the precision, scalability, and transparency of disease risk prediction, this work presents a novel Quantum-Enabled Explainable AI (QXAI) system that syndicates explainable machine learning with quantum computing. Through the use of quantum algorithms, explicitly quantum kernel methods and quantum-enhanced feature selection, the suggested system achieves huge electronic health record (EHR) datasets efficiently while addressing problems with non-linearity and data narrowness. This work uses explainable AI methods like SHAP and counterfactual reasoning to guarantee interpretability, allowing doctors to understand the reasoning behind predictions. Real-world medical datasets are used for a thorough evaluation, which shows that the QXAI system finishes better than traditional baselines in terms of prediction accuracy, model robustness, and explanation reliability. In the end, this paper places the foundation for the implementation of reliable, quantum-assisted decision support systems in medical practice, which will lead to more personalized, reasonable, and knowledgeable healthcare.
Abstract Healthcare organizations have a high volume of sensitive data and traditional technologies have limited storage capacity and computational resources. The prospect of sharing healthcare data for machine learning is more arduous due to firm regulations related to patient privacy. The balanced protection of confidentiality, integrity, and availability of healthcare data, has become a major concern beyond classical data security considerations. In recent years, federated learning offers a solution to accelerate distributed machine learning addressing concerns related to data privacy and governance. Currently, the blend of quantum computing and machine learning has experienced significant attention from academic institutions and research communities. Quantum computers have shown the potential to bring huge benefits to the healthcare sector through efficient distributed training across several quantum nodes. The ultimate objective of this work is to develop a quantum federated learning framework (QFL) to tackle the optimization, security, and privacy challenges in the healthcare and clinical industries for medical imaging tasks. In this work, we proposed federated quantum convolutional neural networks (QCNNs) with distributed training across edge devices. To demonstrate the feasibility of the proposed QFL framework, we performed extensive experiments on medical datasets (Pneumonia MNIST, and CT-kidney disease analysis), which are non-independently and non-identically partitioned among the healthcare institutions/clients. The quantum federated global model maintained a high classification testing accuracy and generalizability and outperformed the locally train clients regardless of how unbalanced the medical data is distributed among the clients. The global model achieved the best performance as compared to local clients in terms of the area under curve of the receiver operating characteristic curve (AUCROC) (0.953) and an average of (0.98) on all classes for predicting outcomes on pneumonia and CT-kidney datasets, respectively. Moreover, the client selection mechanism is proposed to reduce the computation overhead at each communication round, which effectively improves the convergence rate. The proposed quantum federated learning framework is validated and assessed via large-scale simulations. Based on our results from numerical simulations, the deployment of distributed and secure quantum machine learning algorithms for enabling scalable and privacy-preserving intelligent healthcare applications would be extremely valuable.
Early and accurate prediction of cardiovascular disease is essential to improve patient outcomes and reduce healthcare costs. This research presents a hybrid classical–quantum machine learning framework for heart disease prediction using the Cleveland dataset. The proposed pipeline integrates advanced feature engineering, bio-inspired optimization, and quantum-inspired learning to improve classification performance and interpretability. The system applies multiple feature selection techniques followed by a hybrid feature fusion strategy. Orthogonal Component Analysis is then used for dimensionality transformation, while quantum-inspired feature mapping simulates quantum state coding. A feature selection mechanism based on a Genetic Algorithm optimizes the subset of features. Classical and quantum machine learning models are evaluated, including Random Forest, Gradient Boosting, K-Nearest Neighbors, Logistic Regression, Quantum Support Vector Classifier, Variational Quantum Classifier, Quantum KNN, and Quantum Neural Networks. Model performance is evaluated using accuracy metrics. To ensure transparency and trustworthiness, explainable AI techniques such as SHAP, LIME and DiCE are integrated to provide local and global interpretability of predictions. Experimental results demonstrate that the proposed hybrid framework improves predictive performance by achieving 90% accuracy compared to traditional machine learning approaches, while maintaining model explainability. The model achieved an overall accuracy of 90%, indicating strong predictive capability in cardiovascular disease risk classification. A detailed analysis of class-wise performance shows that for Class 0, the model obtained a precision of 0.85, a recall of 0.97, and an F1-score of 0.90, demonstrating excellent ability to correctly identify negative cases with minimal false negatives. For Class 1, the model achieved a precision of 0.96, a recall of 0.84, and an F1-score of 0.90, indicating high confidence in positive predictions, though with slightly lower recall compared to Class 0. This study highlights the potential of combining classical feature engineering, evolutionary optimization and quantum-inspired learning for next-generation medical decision support systems. The integration of quantum-inspired techniques also provides a promising direction for improving computational efficiency and model robustness in healthcare analytics. The findings suggest that hybrid classical–quantum learning approaches can support clinicians in making faster and more reliable diagnostic decisions.