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.
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.
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.
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.
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.
Heart disease continues to be among the top causes of death globally, and thus precise and timely detection mechanisms are necessary for successful clinical intervention. This chapter introduces a sophisticated predictive model integrating quantum-enhanced feature extraction with classical and quantum machine learning (QML) models to enhance prediction accuracy and reliability of heart disease. Quantum models like quantum random forest (QRF), quantum k-nearest neighbor (QKNN), and quantum gradient boosting classifier (QGBC) are used in conjunction with traditional models like XGBoost and SVM to investigate hybrid computational synergies. The method utilizes quantum feature mapping and entanglement-based encoding to identify intricate nonlinear patterns present in cardiovascular data. A thorough performance comparison based on the UCI Heart Disease dataset shows that quantum-augmented models exhibit better predictive accuracy, precision, and F1-scores than conventional models. The findings support the potential of quantum computing to speed up healthcare analytics and make more accurate disease diagnosis possible. This work discusses the pioneering merger of quantum computing and machine learning as a revolutionary strategy for data-driven intelligent medical decision support systems.
Chronic diseases—such as diabetes, cardiovascular disorders, and chronic respiratory conditions—account for over 70% of global deaths annually, with late diagnosis being a primary contributor to poor outcomes. While machine learning (ML) models have shown promise in early detection, they often suffer from limited generalizability, data heterogeneity, and insufficient interpretability in clinical settings. This paper introduces a novel hybrid quantum-classical machine learning (HQML) framework that synergistically combines the pattern recognition power of classical deep neural networks with the high-dimensional optimization capabilities of quantum algorithms to enhance diagnostic accuracy, robustness, and early signal detection in chronic disease prediction. Using multimodal electronic health record (EHR) data—including clinical, genomic, and lifestyle variables—we train a quantum-enhanced feature selector followed by a classical interpretable classifier (e.g., SHAP-augmented XGBoost). The quantum component leverages a variational quantum circuit to identify non-linear, high-order feature interactions that classical methods often miss. Validated on datasets from the National Health and Nutrition Examination Survey (NHANES) and UK Biobank, our model achieves 94.7% AUC for type 2 diabetes prediction five years before clinical diagnosis—outperforming state-of-the-art baselines by 6.2%. Crucially, the framework maintains interpretability through post-hoc explainability and reduces data bias via fairness-aware quantum embedding. This research bridges quantum computing and public health, offering a scalable, ethically grounded diagnostic paradigm. By enabling earlier, more accurate, and equitable predictions, the HQML framework has significant potential to transform preventive care and reduce the global burden of chronic disease.
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.