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.
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.
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 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.
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 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.
Cardiac risk prediction is critical for the early detection and prevention of cardiovascular diseases, a leading global cause of mortality. In response to the growing volume and complexity of healthcare data, there has been increasing reliance on computational approaches to enhance clinical decision-making and improve early detection of cardiac risks. Although classical machine learning techniques have demonstrated strong performance in cardiovascular disease prediction, their efficiency and scalability are increasingly challenged by high-dimensional and large-scale medical datasets. Emerging advances in quantum computing have introduced quantum machine learning (QML) as a promising alternative, offering novel computational paradigms with the potential to outperform classical methods in terms of speed and problem-solving capability. This review analyzed twelve studies, evaluating data types, quantum architecture, performance metrics, and comparative efficacy against classical machine learning models. Our findings indicate that QNNs show promise for enhanced predictive accuracy and computational efficiency. However, significant challenges in scalability, noise resilience, and clinical integration persist. The translation of quantum advantage into clinical practice necessitates further validation on large-scale with diverse datasets.
Early diagnosis of chronic diseases is paramount to improving patient survival rates, optimizing treatment trajectories, and mitigating escalating healthcare expenditures. While the recent explosion of heterogeneous healthcare data presents robust opportunities for predictive modeling, it simultaneously introduces severe computational bottlenecks regarding data dimensionality and non-linear complexity. This paper proposes a novel, high-performance hybrid prediction framework that seamlessly fuses classical machine learning topologies with Variational Quantum Circuits (VQCs) within a Quantum Machine Learning (QML) paradigm. The proposed architecture targets the multi-classification and risk profiling of five high-prevalence chronic conditions: diabetes, cardiovascular disease, breast cancer, chronic liver disease, and chronic kidney disease This project introduces “Quantum Health,” a Quantum Machine Learning (QML)-based multi disease prediction system that integrates classical and quantum computing to enhance diagnostic performance. It employs five quantum-enhanced models: Quantum Random Forest for breast cancer, Quantum Neural Network for diabetes, Quantum k-Nearest Neighbors (QkNN) for heart disease, Quantum AdaBoost for liver disease, and Quantum Naive Bayes for kidney disease. The models are evaluated using both classical methods and PennyLane simulations, enabling direct comparison of performance. The system is deployed as a Flask-based web application featuring a user-friendly dashboard, disease-specific prediction modules, prediction history with CSV export, and an analytics dashboard comparing classical and quantum accuracies. Each module provides predictions, confidence scores, and feature importance insights. Unlike monolithic classical models, our approach incorporates a parameterized quantum layer that maps low-dimensional classical medical vectors into high-dimensional Hilbert spaces, capturing intricate, non-linear correlations that evade classical kernels. Experimental evaluations executed on simulated quantum backends demonstrate that the proposed Quantum Hybrid Model drastically outperforms conventional baselines, achieving an overall classification accuracy of 96.8% and an Area Under the Curve (AUC) of 98.4%. The findings establish that integrating quantum circuit state vectors with ensemble classical architectures provides a scalable, computationally efficient, and highly generalizable paradigm for next-generation clinical decision support systems.