Quantum Computing (QC) works based on the principle of quantum mechanics, which is different from traditional computers. Heart disease remains the leading cause of mortality worldwide and the development of advanced predictive models for early diagnosis is necessary. In this paper, the Support Vector Machine (SVM), Quantum Support Vector Machine (QSVM), and Variational Quantum Classifier (VQC) are used to predict heart disease. Among these algorithms, QSVM and VQC are used for heart disease prediction using QC principles. The SVM and QSVM achieves the accuracy of 80% and 69% and VQC achieves the highest accuracy of 86% with ZFeatureMap and RealAmplitude in 100 iterations. The FeatureMap and Ansatz are used for turning data into quantum states. The VQC employs a parameterized quantum circuit to minimize the classification error and optimization is done using gradient-free COBYLA. In addition to ZFeatureMap, other FeatureMaps like ZZFeatureMap and PauliFeatureMap are also performed in VQC.
Quantum Computing (QC) works based on the principle of quantum mechanics, which is different from traditional computers. Heart disease remains the leading cause of mortality worldwide and the development of advanced predictive models for early diagnosis is necessary. In this paper, the Support Vector Machine (SVM), Quantum Support Vector Machine (QSVM), and Variational Quantum Classifier (VQC) are used to predict heart disease. Among these algorithms, QSVM and VQC are used for heart disease prediction using QC principles. The SVM and QSVM achieves the accuracy of 80% and 69% and VQC achieves the highest accuracy of 86% with ZFeatureMap and RealAmplitude in 100 iterations. The FeatureMap and Ansatz are used for turning data into quantum states. The VQC employs a parameterized quantum circuit to minimize the classification error and optimization is done using gradient-free COBYLA. In addition to ZFeatureMap, other FeatureMaps like ZZFeatureMap and PauliFeatureMap are also performed in VQC.
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.
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 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.
The aim of this study is to enhance the accuracy and reduce the time complexity of predicting cardiac illnesses by utilizing patient vital signs exclusively. The importance of timely treatment in critical situations where quick decisions are necessary is emphasized here. Precise predictions have the potential to prevent health deterioration and even save lives. By analyzing vast datasets (images, texts etc.) and detecting subtle patterns, quantum machine learning algorithms (QML) can offer more accurate results in a shorter period compared to classical algorithms. The feature set used here is a novel one to be used for the quick detection and early prediction of cardiovascular diseases, and also a convenient one for this task. To predict heart diseases or abnormalities, both classical and quantum machine learning techniques have been employed in this paper. We have developed four models based on Support Vector Machine (SVM), Neural Network (NN), Quantum Support Vector Machine (QSVM), and Quantum Neural Network (QNN) and compared their performance on a balanced sample of a vast dataset for heart diseases prediction. After comparing the models' performance, we found that Quantum Support Vector Machine (QSVM) performed best with an accuracy of 75% and to-fold Cross-validation score of 80%.
Nowadays, quantum machine learning is attracting great interest in a wide range of fields due to its potential superior performance and capabilities. The massive increase in computational capacity and speed of quantum computers can lead to a quantum leap in the healthcare field. Heart disease seriously threatens human health since it is the leading cause of death worldwide. Quantum machine learning methods can propose effective solutions to predict heart disease and aid in early diagnosis. In this study, an ensemble machine learning model based on quantum machine learning classifiers is proposed to predict the risk of heart disease. The proposed model is a bagging ensemble learning model where a quantum support vector classifier was used as a base classifier. Furthermore, in order to make the model’s outcomes more explainable, the importance of every single feature in the prediction is computed and visualized using SHapley Additive exPlanations (SHAP) framework. In the experimental study, other stand-alone quantum classifiers, namely, Quantum Support Vector Classifier (QSVC), Quantum Neural Network (QNN), and Variational Quantum Classifier (VQC) are applied and compared with classical machine learning classifiers such as Support Vector Machine (SVM), and Artificial Neural Network (ANN). The experimental results on the Cleveland dataset reveal the superiority of QSVC compared to the others, which explains its use in the proposed bagging model. The Bagging-QSVC model outperforms all aforementioned classifiers with an accuracy of 90.16% while showing great competitiveness compared to some state-of-the-art models using the same dataset. The results of the study indicate that quantum machine learning classifiers perform better than classical machine learning classifiers in predicting heart disease. In addition, the study reveals that the bagging ensemble learning technique is effective in improving the prediction accuracy of quantum classifiers.
To forecast heart problems, an innovative hybrid technique combining quantum computing with machine learning approaches is presented. For the preprocessing stage, normalization and dimensionality reduction techniques are used on a dataset of 270 patients' records and 13 clinical variables to make them appropriate for a 4-qubit quantum computer. Some traditional machine learning algorithms, including convolutional neural networks (CNN), support vector machines (SVM), and XGBoost, are trained to establish baselines in relation to other methods. Under the guidance of the SPSA algorithm, a quantum-inspired classifier called VQC is developed using a parameterized quantum circuit that includes rotation and entanglement operations. A quantitative study of crucial performance metrics reveals that the suggested quantum technique outperforms conventional methods in tests. The VQC classifier has the highest accuracy (85.2%), precision (86.4%), and F1 score (82.6%), while matching the SVM classifier's ROC-AUC score of 88.9%. Although with lower dimensionality, this research shows how quantum-based machine learning may find complex correlations among variables. This study is intended to serve as a preliminary concept for using quantum machine learning for healthcare applications, especially for increasing the accuracy rate and lowering misdiagnosis errors.