Cardiovascular diseases remain the leading cause of mortality globally, necessitating accurate diagnostic tools for early detection and intervention. This paper presents a comprehensive comparative and hybrid analysis of quantum and classical machine learning models for heart disease prediction using the UCI Cleveland dataset. We implement classical baseline models including Logistic Regression, Random Forest, Support Vector Machine (SVM), and XGBoost, alongside quantum models: Quantum Support Vector Machine (QSVM) with statevector fidelity kernels, Variational Quantum Classifier (VQC) with data-reuploading ansätze, and Quantum Convolutional Neural Networks (QCNN) with parametric pooling. The primary contribution is the development of a hybrid quantum classical ensemble meta-classifier that fuses predictions from all classical and quantum learners using XGBoost as a meta learner. Experiments conducted in Google Colab using Qiskit 1.2+ and Penny Lane 0.36+ demonstrate that the proposed hybrid ensemble achieves superior performance with 98.33accuracy, 100perfect AUC-ROC of 1.0, substantially outperforming standalone classical models (best: 88.33(best: 80hybrid model’s superiority. These results underscore the potential of quantum feature spaces combined with classical decision boundaries in healthcare prediction systems, and demonstrate the immediate utility of hybrid quantum-classical integration in medical diagnostics on near-term quantum hardware.
Cardiovascular diseases remain the leading cause of mortality globally, necessitating accurate diagnostic tools for early detection and intervention. This paper presents a comprehensive comparative and hybrid analysis of quantum and classical machine learning models for heart disease prediction using the UCI Cleveland dataset. We implement classical baseline models including Logistic Regression, Random Forest, Support Vector Machine (SVM), and XGBoost, alongside quantum models: Quantum Support Vector Machine (QSVM) with statevector fidelity kernels, Variational Quantum Classifier (VQC) with data-reuploading ansätze, and Quantum Convolutional Neural Networks (QCNN) with parametric pooling. The primary contribution is the development of a hybrid quantum classical ensemble meta-classifier that fuses predictions from all classical and quantum learners using XGBoost as a meta learner. Experiments conducted in Google Colab using Qiskit 1.2+ and Penny Lane 0.36+ demonstrate that the proposed hybrid ensemble achieves superior performance with 98.33accuracy, 100perfect AUC-ROC of 1.0, substantially outperforming standalone classical models (best: 88.33(best: 80hybrid model’s superiority. These results underscore the potential of quantum feature spaces combined with classical decision boundaries in healthcare prediction systems, and demonstrate the immediate utility of hybrid quantum-classical integration in medical diagnostics on near-term quantum hardware.
Heart disease is one of the major global causes of death, requiring early and precise detection for efficient treatment.With advancements in quantum computing, Variational Quantum Circuits (VQCs) have gained recognition as a valuable technique for improving learning models in medical diagnosis.This study further explores the application of VQC-based models for heart disease prediction, leveraging quantum computing to enhance classification accuracy.A hybrid quantum-classical model has been developed, which combines classical preprocessing for feature extraction from patient datasets with a VQC-based classifier for medical predictions.The quantum circuit using variational parameters is optimized to reduce the loss function and facilitate effective learning from complex medical data.This proposed approach is tested on benchmark heart disease datasets and evaluated against classical Machine Learning (ML) models, including Support Vector Machines (SVMs), Random Forests, and Neural Networks.The experimental results show that VQC-based models achieve high accuracy and outperform classical models, notably on high-dimensional, non-linearly separable data.The quantum advantage of VQCs lies in their ability to encode and process data in a huge Hilbert space.However, challenges such as noise in near-term quantum devices and scalability issues remain key.This research underscores the efficacy of VQCbased quantum machine learning in medical diagnosis, paving the way for future studies on quantum-enhanced healthcare solutions.Further advancements in quantum hardware and hybrid learning techniques could allow quantum-assisted heart disease prediction to become an effective clinical tool.
Heart disease remains one of the most critical global health concerns, creating a strong need for intelligent and accurate prediction systems. Even while conventional machine learning (ML) has shown promise, handling large and complex medical datasets often reduces the efficiency of traditional machine learning systems. Using a cloud-supported quantum framework, we present a hybrid diagnostic framework that compares three quantum architectures—QSVM, VQC, and QCNN—with five classical algorithms: Random Forest, SVM, KNN, Logistic Regression, and Gradient Boosting. The framework includes a cloud-managed preprocessing stage that uses PCA to map high-impact features, like blood pressure and cholesterol, onto an optimized qubit space in order to overcome the resource limitations of existing NISQ devices. To test the models, the Heart-Disease data set was applied, where classical algorithms demonstrated exceptional performance ranging between 70% to 75%. Specifically, because of the high precision of 0.81, VQC proved to possess remarkable performance on optimized data sets.
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.
Heart disease remains a leading cause of mortality globally, driving interest in advanced computational methods forearly prediction. This study presents a comparative analysis of classical machine learning techniques versus a QuantumSupport Vector Machine (QSVM) for predicting heart disease using a specialized dataset featuring standard clinicalmetrics alongside a unique 'QuantumPatternFeature. 'We implemented and evaluated six classical algorithms (LogisticRegression, K-Nearest Neighbors, Decision Tree, Random Forest, Naive Bayes, Gradient Boosting), employingRandomizedSearchCV for hyperparameter optimization. For the quantum approach, a QSVC utilizing Qiskit'sZZFeatureMap (reps=2) and FidelityQuantumKernel was trained and tested using a classical simulator. A classicalSupport Vector Classifier (SVC) with an RBF kernel served as an additional baseline. Performance was assessed on a20% hold-out test set using accuracy, precision, recall, F1-score, and confusion matrices. Results indicate that tunedclassical models, particularly Logistic Regression and Gradient Boosting, achieved the highest accuracy (93.0%),significantly outperforming the simulated QSVC (79.0%). The classical SVC baseline also surpassed the QSVM with89.0% accuracy. Furthermore, the QSVM exhibited substantially higher computational times for both training andprediction on the simulator compared to all classical methods. The findings suggest that for this dataset size and thespecific simulated QSVM configuration, classical machine learning approaches offer superior performance andefficiency, and the 'QuantumPatternFeature' did not confer a discernible advantage within this quantum implementationframework.
Cardiovascular diseases remain a leading global health challenge, driving the need for advanced, accurate diagnostic tools. While classical machine learning (ML) models like XGBoost have achieved significant success in medical diagnosis, they often face challenges related to accuracy plateaus and model explainability in complex, high-dimensional data spaces. Quantum machine learning (QML) emerges as a promising paradigm to potentially overcome these limitations by leveraging quantum computational advantages. This study aims to develop and evaluate a hybrid quantum-classical pipeline for the early detection of heart disease. We integrate robust data preprocessing—including missing value imputation, feature engineering, SMOTE for class balancing, and PCA for dimensionality reduction—with two classification approaches: a classical XGBoost model and a quantum Variational Quantum Classifier (VQC). Our results demonstrate that the XGBoost model achieves strong performance with an accuracy of 88.52% and an F1-score of 0.8852 on an imbalanced test set. While the VQC model did not surpass the classical baseline in the current noisy simulation environment, it shows theoretical promise. This work highlights the practical challenges of current QML and outlines a clear pathway for future research towards achieving a quantum advantage in clinical decision support systems.
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.