Heart diseases are a set of heart and blood-vessel-related disorders, arising from a variety of lifestyle, environmental, and genetic factors. Even with advancements in medical technology, accurately forecasting heart attack risk remains challenging, as today’s diagnosis methods fail to catch the nonlinearity and complexity in clinical data. Classical machine learning algorithms, such as k-nearest neighbors and support vector machine, when mixed with synthetic minority oversampling technique, show promise for heart attack risk prediction. These models for real-world scenarios struggle with high-dimensional, imbalanced, nonlinear datasets, which limits the effectiveness of models. Quantum machine learning offers a computational approach and takes benefit of concepts like superposition and entanglement to uncover insightful patterns. In this work, we compare classical and quantum models for heart attack risk prediction using a heart attack prediction dataset with 14 clinical features and 1888 patient records. The outcomes demonstrate that the classical support vector machine, when combined with synthetic minority oversampling technique, achieves the highest predictive accuracy of 92.00%, with minimal run time. k-nearest neighbors also works well, with an accuracy of 83.33%. Among quantum methods, quantum support vector machine with quantum Fourier transform achieves a 74.00% accuracy, while quantum clustering reaches 67.20%. However, both approaches required higher computational power. The quantum Fourier transform–based approach improves quantum model performance, particularly in recognizing cyclic trends in clinical data. However, classical approaches remain superior with respect to accuracy and efficiency on current hardware. This work demonstrates the strength of quantum machine learning in handling complex data while also recognizing the present challenges with quantum hardware and simulators. It suggests that upcoming research should focus on testing these models on actual quantum devices. Adopting standardized performance metrics can improve both classical and quantum-based decision support systems for heart disease risk prediction.
Heart diseases are a set of heart and blood-vessel-related disorders, arising from a variety of lifestyle, environmental, and genetic factors. Even with advancements in medical technology, accurately forecasting heart attack risk remains challenging, as today’s diagnosis methods fail to catch the nonlinearity and complexity in clinical data. Classical machine learning algorithms, such as k-nearest neighbors and support vector machine, when mixed with synthetic minority oversampling technique, show promise for heart attack risk prediction. These models for real-world scenarios struggle with high-dimensional, imbalanced, nonlinear datasets, which limits the effectiveness of models. Quantum machine learning offers a computational approach and takes benefit of concepts like superposition and entanglement to uncover insightful patterns. In this work, we compare classical and quantum models for heart attack risk prediction using a heart attack prediction dataset with 14 clinical features and 1888 patient records. The outcomes demonstrate that the classical support vector machine, when combined with synthetic minority oversampling technique, achieves the highest predictive accuracy of 92.00%, with minimal run time. k-nearest neighbors also works well, with an accuracy of 83.33%. Among quantum methods, quantum support vector machine with quantum Fourier transform achieves a 74.00% accuracy, while quantum clustering reaches 67.20%. However, both approaches required higher computational power. The quantum Fourier transform–based approach improves quantum model performance, particularly in recognizing cyclic trends in clinical data. However, classical approaches remain superior with respect to accuracy and efficiency on current hardware. This work demonstrates the strength of quantum machine learning in handling complex data while also recognizing the present challenges with quantum hardware and simulators. It suggests that upcoming research should focus on testing these models on actual quantum devices. Adopting standardized performance metrics can improve both classical and quantum-based decision support systems for heart disease risk prediction.
Cardiovascular disease is one of the most common reasons of increases the global death rate, suggesting initial detection and timely treatment crucial. While traditional machine learning models have been effective in predicting heart disease, Quantum Computing (QC) introduces new possibilities for improving accuracy and processing speed. In this regard the application of Quantum Machine Learning (QML) approaches-categorically, Quantum Neural Networks (QNN) and Quantum Support Vector Classification (QSVC)-to predict heart disease using key health indicators such as present-age, levels of cholesterol, Blood Pressure (BP), and lifestyle choices. QNN is utilized to recognize complex patterns in patient data, leveraging deep learning to understand intricate connections between various risk factors. Meanwhile, QSVC takes advantage of QC ability to compute large amounts of information at once, making classification faster and more precise. Both models are trained and tested on a heart disease dataset and compared with traditional machine learning methods to assess their accuracy, efficiency, and scalability. The results show that while quantum and classical models achieve similar accuracy, quantum computing significantly reduces processing time, making it highly efficient for analyzing large datasets. This study highlights the strength of QC in transforming medical diagnostics, offering faster and more reliable predictive models for early heart disease detection. By integrating quantum algorithms into healthcare, this research contributes to the advancement of AI-driven medical solutions, resulting in more efficient and individual patient care.
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%.
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.
Coronary heart disease (CHD) is a severe cardiac disease, and hence, its early diagnosis is essential as it improves treatment results and saves money on medical care. The prevailing development of quantum computing and machine learning (ML) technologies may bring practical improvement to the performance of CHD diagnosis. Quantum machine learning (QML) is receiving tremendous interest in various disciplines due to its higher performance and capabilities. Techniques for QML have the potential to forecast cardiac disease and help in early detection. To predict the risk of coronary heart disease, a hybrid approach utilising an ensemble machine learning model based on QML classifiers is presented in this paper. Our approach, with its unique ability to address multidimensional healthcare data, reassures the method’s robustness by fusing quantum and classical ML algorithms in a multi-step inferential framework. Reducing cardiac morbidity and mortality requires early detection of heart disease. In this research, a hybrid approach utilises techniques with quantum computing capabilities to tackle complex problems that are not amenable to conventional ML algorithms and to minimise computational expenses. The proposed method has been developed in the Raspberry Pi 4B Graphics Processing Unit (GPU) platform and tested on a broad dataset that integrates clinical and imaging data from patients suffering from CHD and healthy controls. The proposed research is developed with a hybrid approach that combines different machine learning algorithms, such as KNN + RF, DT + RF, LR + RF, and Adaboost + RF, for diagnosing coronary illness with higher accuracy through feature selection. The proposed system performance obtained an accuracy of 99%, utilising 20000 datasets with 14 attributes from various datasets collected from the Local Pathology Lab in the Muzaffarnagar District of Uttar Pradesh, India. Compared to classical machine learning models, the accuracy, sensitivity, F1 score, and specificity of the proposed hybrid QML model used with CHD are manifold higher.
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.