Cardiovascular diseases are the most dominant cause of mortality across the entire world, claiming 17.9 million lives every year. According to recent trends, the mortality rate as a result of heart failure continues to increase particularly in low- and middle-income areas which means that early diagnostic tools desperately need to be discovered. Conventional biomarkers like NT-proBNP, troponins and creatinine levels are useful, although their sensitivity and specificity are low, particularly in identifying heart failure onset before it is actually clinically apparent. We suggest addressing these issues by proposing the following Hybrid Quantum-Classical Neural Network with Heuristics-Based Feature Selection that can be used to predict the onset of heart failure: H-HQNN: A Hybrid Quantum-Classical Neural Network with Heuristics-Based Feature Selection. Given this, SMOTE is used to balance the clinical data and reduce class imbalance, whereas the most informative cardiac biomarkers and clinical features are selected using a two-stage process in which Recursive Feature Elimination (RFE) first prunes redundant attributes, followed by a dual-phase optimization where PSO refines the feature subset and Cuckoo Search (CS) enhances global exploration to avoid local minima, resulting in a compact and highly relevant feature set. These features are then represented in a variational quantum circuit (4 qubits) and are implemented as ZZFeatureMap, Pauli-Z gates, EfficientSU2, and RealAmplitudes, which are combined in a hybrid quantum-classical neural network. Performance was estimated using stratified 5-fold cross-validation on a single public dataset; external validation on independent cohorts has not yet been performed. The average accuracy of our model is 96.62% ± 3.45 with precision of 96.11, recall of 96.10, F1-score of 96.54 and AUC = 0.9604. The most efficient fold has 98.30% accuracy and AUC = 0.9872 with a confusion matrix of almost perfect balance in true positive and true negative rates. H-HQNN is more efficient and interpretable as it achieves better discriminative power, with fewer features, compared to classical baselines (e.g., Random Forests, deep neural networks). H-HQNN presents an opportunity toward novel methodology of identifying early heart failure by utilizing quantum-classical hybrid modeling and heuristic feature selection to ensure progress in preventative cardiology and therapeutic intervention in particular where advanced imaging studies or invasive measurements of biomarkers are not feasible.
Cardiovascular diseases are the most dominant cause of mortality across the entire world, claiming 17.9 million lives every year. According to recent trends, the mortality rate as a result of heart failure continues to increase particularly in low- and middle-income areas which means that early diagnostic tools desperately need to be discovered. Conventional biomarkers like NT-proBNP, troponins and creatinine levels are useful, although their sensitivity and specificity are low, particularly in identifying heart failure onset before it is actually clinically apparent. We suggest addressing these issues by proposing the following Hybrid Quantum-Classical Neural Network with Heuristics-Based Feature Selection that can be used to predict the onset of heart failure: H-HQNN: A Hybrid Quantum-Classical Neural Network with Heuristics-Based Feature Selection. Given this, SMOTE is used to balance the clinical data and reduce class imbalance, whereas the most informative cardiac biomarkers and clinical features are selected using a two-stage process in which Recursive Feature Elimination (RFE) first prunes redundant attributes, followed by a dual-phase optimization where PSO refines the feature subset and Cuckoo Search (CS) enhances global exploration to avoid local minima, resulting in a compact and highly relevant feature set. These features are then represented in a variational quantum circuit (4 qubits) and are implemented as ZZFeatureMap, Pauli-Z gates, EfficientSU2, and RealAmplitudes, which are combined in a hybrid quantum-classical neural network. Performance was estimated using stratified 5-fold cross-validation on a single public dataset; external validation on independent cohorts has not yet been performed. The average accuracy of our model is 96.62% ± 3.45 with precision of 96.11, recall of 96.10, F1-score of 96.54 and AUC = 0.9604. The most efficient fold has 98.30% accuracy and AUC = 0.9872 with a confusion matrix of almost perfect balance in true positive and true negative rates. H-HQNN is more efficient and interpretable as it achieves better discriminative power, with fewer features, compared to classical baselines (e.g., Random Forests, deep neural networks). H-HQNN presents an opportunity toward novel methodology of identifying early heart failure by utilizing quantum-classical hybrid modeling and heuristic feature selection to ensure progress in preventative cardiology and therapeutic intervention in particular where advanced imaging studies or invasive measurements of biomarkers are not feasible.
Heart disease morbidity and mortality rates are increasing, which has a negative impact on public health and the global economy. Early detection of heart disease reduces the incidence of heart mortality and morbidity. Recent research has utilized quantum computing methods to predict heart disease with more than 5 qubits and are computationally intensive. Despite the higher number of qubits, earlier work reports a lower accuracy in predicting heart disease, have not considered the outlier effects, and requires more computation time and memory for heart disease prediction. To overcome these limitations, we propose hybrid random forest quantum neural network (HQRF) using a few qubits (two to four) and considered the effects of outlier in the dataset. Two open-source datasets, Cleveland and Statlog, are used in this study to apply quantum networks. The proposed algorithm has been applied on two open-source datasets and utilized two different types of testing strategies such as 10-fold cross validation and 70-30 train/test ratio. We compared the performance of our proposed methodology with our earlier algorithm called hybrid quantum neural network (HQNN) proposed in the literature for heart disease prediction. HQNN and HQRF outperform in 10-fold cross validation and 70/30 train/test split ratio, respectively. The results show that HQNN requires a large training dataset while HQRF is more appropriate for both large and small training dataset. According to the experimental results, the proposed HQRF is not sensitive to the outlier data compared to HQNN. Compared to earlier works, the proposed HQRF achieved a maximum area under the curve (AUC) of 96.43% and 97.78% in predicting heart diseases using Cleveland and Statlog datasets, respectively with HQNN. The proposed HQRF is highly efficient in detecting heart disease at an early stage and will speed up clinical diagnosis.
Heart diseas remains major cause of death in world wide and early diagnosis is vital to effective clinical treatment. Models that are capable of capturing complex patterns in medical data are needed in Heart disease prediction. This paper presents a hybrid quantum-classical setup that consists of a Quantum Neural Network (QNN), Quantum SVM kernel modeling and classical learners stacked framework. In the experiment with the cleaned UCI heart disease dataset and 10-fold cross-validation, the proposed model has an accuracy of 91.48, F1-score of 0.8955 and AUC of 0.9395, surpassing eight classical baselines. SHAP and LIME are also interpretable, which emphasizes the most influential features. A low scale of samples is also run on IBM Quantum hardware where mitigation is performed by resiliency level to ensure pragmatic feasible outcome. The findings show that quantum components can improve nonlinear feature representation, which proves the possibilities of hybrid quantumbased medical diagnosis.
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.
This research presents a novel quantum-hybrid machine learning framework for heart disease classification that integrates Quantum Neural Networks with variational circuits and Quantum Support Vector Machines featuring custom quantum kernels alongside classical ensemble methods. The methodology employs advanced feature engineering, multi-strategy missing value imputation, and combined feature selection techniques on an enhanced dataset of 2,000 cardiovascular cases with 17 engineered features. The quantum components utilize 8-10 qubits across 5-6 variational layers with quantum interference effects for enhanced pattern recognition capabilities. Through weighted soft voting ensemble strategy and dynamic threshold optimization, the quantum-hybrid system achieved exceptional performance metrics including 96.2% accuracy, 95.8% F1-score, 94.7% precision, and 96.9% recall, significantly exceeding the 95% accuracy target. The quantum models demonstrated measurable advantages over classical approaches, with the Quantum Neural Network reaching 92.4% accuracy and Quantum SVM achieving 90.8% accuracy independently. The hybrid ensemble showed 4.3 percentage points improvement over the best individual classical model and 2.7 percentage points over standalone quantum implementations. This quantum-hybrid approach establishes superior diagnostic capabilities for cardiovascular disease prediction, particularly in minimizing false negatives critical for patient safety, positioning quantum-enhanced machine learning as a viable solution for next-generation medical diagnostic systems with clinically relevant accuracy improvements for healthcare applications.
This study proposes a novel approach to heart disease prediction using quantum neural networks, which addresses the cardiovascular disease as the world's biggest cause of mortality, a global challenge. By leveraging quantum computing capabilities, our system aims to outperform conventional decision support systems in identifying early indicators of heart disease before clinical symptoms appear. The research explores how quantum neural networks, which are capable of handling multiple quantum states simultaneously, can more effectively handle complicated medical data than traditional methods. Our approach combines deep learning techniques that eliminate the need for manual feature extraction, thereby improving both resource and time efficiency. The method analyses important risk factors, including diabetes, LDL cholesterol, HDL levels, and triglycerides, to predict disease likelihood using quantum variation algorithms and random forest regression. This interdisciplinary Research advances the expanding field of artificial intelligence powered cardiology, promising more accurate and early diagnoses that are essential for effective treatment. The proposed system has significant potential for medical applications, ultimately aiming to reduce mortality rates and improve patient outcomes through advanced quantum computing technologies.
Abstract Heart disease remains to be a primary cause of death globally, requiring precise and timely predictive methodologies. Traditional machine learning methods that depend exclusively on classical clinical variables frequently have restricted predictive accuracy and do not adequately use the intricate nonlinear interactions present in patient data. This work examines the effectiveness of hybrid quantum-classical models for predicting heart disease by combining classical clinical data with quantum-derived representations via Variational Quantum Classifier (VQC), Estimator, and Sampler models. Twelve hybrid configurations, integrating three quantum feature maps (Z, ZZ, Pauli) with four ansatz architectures (RealAmplitudes, TwoLocal, EfficientSU2, PauliTwoDesign), were assessed inside the PennyLane and Qiskit frameworks. The results indicate that the VQC model in PennyLane reached optimal performance with Z–TwoLocal and ZZ–PauliTwoDesign configurations, achieving accuracy and F1-scores of up to 0.95. The Estimator model consistently produced reliable predictions, with Pauli–EfficientSU2 and Z–TwoLocal attaining accuracies of up to 0.91 and F1-scores of 0.92 across several frameworks. The Sampler model demonstrated 1reasonable performance, with the Pauli–EfficientSU2 configuration achieving an accuracy of 0.65 and an F1-score of 0.73, underscoring the supplementary role of stochastic quantum sampling. The analysis of feature significance indicated that the quantum-derived feature provided the most robust predictive signal, exceeding classical variables like Age, Blood Pressure, and Cholesterol. These findings demonstrate that meticulous alignment of feature maps and ansatz circuits is essential for optimizing model expressivity, convergence, and predictive effectiveness. In summary, hybrid quantum-classical models, especially the Estimator and VQC architectures, significantly improve heart disease prediction, providing a feasible method for precision clinical decision support and potential incorporation into privacy-preserving, multi-institutional healthcare systems.