Abstract Quantum machine learning (QML) is an emerging field that combines the power of quantum computing with machine learning (ML) techniques to solve complex problems. In recent years, QML algorithms have shown tremendous potential in various applications such as image recognition, natural language processing, health care, finance, and drug discovery. QML algorithms aim to reduce computation costs and solve complex problems beyond the scope of classical machine learning algorithms. In this article, we study the performance of two QML algorithms, that is, quantum support vector classifiers (QSVC) and variational quantum classifiers (VQC), for chronic heart disease prediction in Healthcare 4.0. The performance of the two classifiers is assessed using different evaluation metrics like accuracy, precision, recall, and F1 score. The authors concluded the superior performance of QSVC over VQC with an accuracy of 82%.
Abstract Quantum machine learning (QML) is an emerging field that combines the power of quantum computing with machine learning (ML) techniques to solve complex problems. In recent years, QML algorithms have shown tremendous potential in various applications such as image recognition, natural language processing, health care, finance, and drug discovery. QML algorithms aim to reduce computation costs and solve complex problems beyond the scope of classical machine learning algorithms. In this article, we study the performance of two QML algorithms, that is, quantum support vector classifiers (QSVC) and variational quantum classifiers (VQC), for chronic heart disease prediction in Healthcare 4.0. The performance of the two classifiers is assessed using different evaluation metrics like accuracy, precision, recall, and F1 score. The authors concluded the superior performance of QSVC over VQC with an accuracy of 82%.
Quantum computing is fast-growing and progressive technology that provides the best results in various domains – including healthcare. In this paper, the Quantum Machine Learning (QML) algorithms, namely Quantum Support Vector Classifiers (QSVC) and Quantum Convolutional Neural Networks (QCNN), are employed for classifying cardiovascular diseases using the Cardiovascular dataset from Kaggle. In contrast with conventional models, the QML algorithms make explicit use of superposition and entanglement to compress and process highly complex and high-dimensional data. In the QSVC approach, patient data are transformed into quantum space with higher-dimensional features by quantum feature maps. This transformation aids QSVC in the process of identifying the complex patterns which are missed in traditional ML methods. In QCNN, the quantum circuits are combined with convolutional layers to capture the understandable features and improve its ability in classification. Both methods are deployed on performance metrics such as Accuracy, Precision, Recall, F1-score and AUC-ROC to show the potential of the methods. The accuracy of the produced QCNN (92%) is greater than that of QSVC (87%). The quantum models demonstrate superior performance regarding metrics and precision level as a result of their rapid convergence. A medical diagnostic revolution occurs through QML algorithms enabling classification between healthy and unhealthy cardiovascular patterns.
This paper explores the use of quantum-based machine learning algorithms for predicting heart disease. Traditional methods face challenges in processing complex healthcare data efficiently. To address this, quantum computing techniques, including dimensionality reduction and feature optimization, are applied. Quantum machine learning models, such as Variational Quantum Classifier (VQC), Quantum Support Vector Classification (QSVC) and Quantum Neural Networks (QNN), leverage these enriched datasets. This convergence of quantum computing and healthcare has the potential to transform medical care, cut costs, and save lives, presenting a significant advancement in predictive medicine.
Quantum machine learning (QML) is a rising research field that is an intersection of two major domains -quantum computing and machine learning (ML). In recent times, it has been employed to solve complex problems in domains such as image processing, healthcare, drug discovery, natural language processing and finance. The advantages of QML algorithms over traditional ML models include reduced computation resources and the ability to handle and solve complex problems as well as multiple parameters. In this study, we employ QML models for lung cancer prediction in the smart healthcare system domain. We adopted QML approaches, i.e., VQC and Pegasos QSVC, to improve the detection efficiency of lung cancer. We used different performance parameters, such as training accuracy and statistical measures, to evaluate the QML models and also performed a comparative study with the existing baseline models. We analyse that the Pegasos QSVC model outperforms other QML models with a classification accuracy of 85%.
Abstract Accurate early identification and treatment of cardiovascular diseases can prevent heart failure problems and reduce mortality rates. This study aims to use quantum learning to predict heart problems to increase the accuracy of traditional prediction and classification methods. Machine learning (ML) and deep learning (DL) techniques need quantum learning to quickly and accurately analyze massive volumes of complex data. With quantum computing, the suggested DL and ML algorithms can change their predictions on the basis of changes in the dataset. This approach could help with the early and accurate detection of chronic diseases. The Cleveland heart disease dataset is undergoing preliminary processing to validate missing values to increase the precision rate and prevent incorrect forecasts. This study examined the feasibility of employing and deploying a quantum ML (QML) framework via cloud computing to categorize cardiac conditions. The research was divided into four sections. First, the principal component analysis was used to preprocess the Cleveland dataset, recursive feature elimination was used to select features, and min–max normalization was used to give the dataset a high-dimensional value. Second, we compared traditional classifiers, such as support vector machine (SVM) and artificial neural network, with the quantum approach to verify the quantum approach’s efficiency. Third, we examined two unique QML classification methods: quantum neural networks (QNNs) and quantum SVM (QSVM). Fourth, bagging-QSVM was developed and deployed as an ensemble learning model. Experimental results using the QNN show an accuracy of 77%, a precision of 76%, a recall of 73%, and an F 1 score of 75%. With an accuracy of 85%, a precision of 79%, a recall of 90%, and an F 1-score of 84%, the QSVM method demonstrated a much better performance than the QNN. Particularly, the Bagging_QSVM model exhibited an outstanding performance, with a flawless score of 100% across all critical performance measures. The study shows that the bagging method for ensemble learning is a solid way of increasing the accuracy of quantum method predictions.
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.
Heart failure (HF) is a critical global health problem, and early diagnosis with accuracy is highly essential for effective treatment. the combination of machine learning (ML) with quantum physics has driven research into quantum algorithms for data processing, offering new methods in novel computational environments. While quantum computing is still in its infancy, with quantum algorithms being actively tested and developed, this study proposes a Quantum Machine Learning (QML) approach for HF prediction using the Pegasos Quantum Support Vector Classifier (QSVC) algorithm. Addressing a critical research gap—the lack of direct comparisons between this algorithm and classical ML algorithms in HF — this study conducts a comprehensive evaluation of Pegasos (Primal Estimated sub-GrAdient SOlver for SVM) QSVC against classical models, including Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and k-Nearest Neighbors (K-NN), using a real-world electronic health dataset. The proposed work utilizes quantum feature maps and kernels to improve HF prediction. the results showed that Pegasos QSVC leveraging quantum principles has potential in handling high dimensional data. The results highlight complementary strengths of the quantum and classical approaches. This work establishes the foundation for further QML research highlighting the technologies potential to transform healthcare predictive modeling.