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.
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.
Quantum-Driven Predictive Healthcare represents a pioneering integration of quantum computing principles with advanced risk modeling and artificial intelligence to revolutionize medical decision support systems. This approach harnesses quantum algorithms to process vast, complex healthcare datasets, enabling rapid identification of subtle patterns and correlations that traditional computing systems may overlook. By incorporating quantum-enhanced risk modeling, clinicians can benefit from more precise predictions of disease progression and patient outcomes, ultimately facilitating early intervention and personalized treatment strategies. AI-powered decision support further refines these predictions by dynamically adapting to new data inputs, learning continuously from real-time clinical feedback. The synergy between quantum computing and AI not only accelerates data analysis but also improves diagnostic accuracy and resource allocation within healthcare institutions. Moreover, the methodology promotes a deeper understanding of multifactorial health conditions by integrating diverse datasets—from genomics to electronic health records—into a coherent analytical framework. This fusion of technologies is poised to overcome the limitations of conventional statistical models, offering a robust, scalable, and future-proof solution for predictive healthcare. As healthcare challenges become increasingly complex, embracing quantum-driven solutions will be instrumental in transforming medical risk assessment and decision-making processes. The research presented herein lays the groundwork for next-generation clinical tools that merge computational innovation with practical healthcare applications, promising enhanced patient outcomes and more efficient medical care delivery.
Abstract Chronic disease prediction presents ongoing challenges in healthcare, primarily due to the complexity of medical data and the need for models that are both accurate and interpretable. This study introduces a quantum-enhanced machine learning model specifically designed for the prediction of hypertension, combining quantum feature transformation with classical algorithms to deliver precise and reliable results. The model demonstrates high performance, achieving an accuracy of 98.40%, precision of 99.3%, recall of 98.6%, and an F1-score of 98.9%. To ensure transparency and facilitate clinical interpretation, explainable AI (XAI) techniques are employed through SHAP values, highlighting critical factors such as hypertension drug usage, age, ferritin, and cholesterol levels as key contributors to hypertension prediction. This quantum-based approach exemplifies the potential for leveraging cutting-edge technologies in healthcare, offering a robust solution that not only ensures predictive accuracy but also supports interpretability—essential for informed clinical decision-making. The integration of quantum computing and explainable machine learning represents a promising step forward in the development of predictive models tailored to complex medical datasets.
Early and accurate disease prediction is a major challenge in modern healthcare systems. Delayed diagnosis often leads to higher treatment costs and lower patient survival rates. Artificial Intelligence (AI) and Machine Learning (ML) techniques are widely used to help with medical decision-making by analyzing complex healthcare datasets. However, traditional machine learning models often face issues with inefficient feature selection, poor hyperparameter tuning, and slow convergence during optimization. This is especially true when working with high-dimensional medical data. To tackle these challenges, this paper presents an AI-based disease prediction framework that uses quantum-inspired optimization techniques. This approach combines classical machine learning classifiers with optimization strategies based on quantum computing principles, such as probabilistic state representation and superposition-based search. These quantum-inspired methods allow for efficient exploration of the solution space, which leads to better feature selection and optimized model parameters. We evaluate the proposed framework using a publicly available healthcare dataset from Kaggle. We compare the performance of traditional machine learning models and quantum-inspired optimized models using accuracy, precision, recall, and F1-score metrics. The experimental results show that the quantum-inspired optimized model consistently performs better than conventional approaches. This study demonstrates that quantum-inspired optimization provides a practical and scalable solution for improving AI-driven disease prediction systems without the need for actual quantum computing hardware.
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.
The rapid growth of healthcare data demands predictive models capable of handling complexity, noise, and imbalance inherent in clinical datasets. This study explores the integration of quantum computing with artificial intelligence to develop Hybrid Quantum–AI models for healthcare prediction. The proposed framework combines classical preprocessing and feature extraction with quantum neural networks (QNNs) and quantum kernel methods to enhance predictive performance on diverse healthcare tasks, including disease diagnosis and postoperative complication prediction. Empirical evaluation across benchmark and clinical datasets demonstrates that hybrid quantum models achieve superior sensitivity, precision, and calibration compared to traditional machine learning approaches, particularly under conditions of data imperfection and small sample size. Beyond performance, this work investigates practical barriers to clinical adoption, including hardware limitations, scalability, interpretability, and ethical compliance. The results highlight that while current quantum hardware remains a constraint, hybrid approaches already offer tangible benefits in predictive accuracy and robustness. The study concludes by outlining a roadmap for real-world implementation, emphasizing the need for interpretable hybrid architectures, federated data strategies, and regulatory alignment to enable the transition of quantum–AI healthcare solutions from research to clinical practice.
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.