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%.
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%.
The combination of quantum computing with machine learning which forms quantum machine learning (QML) presents promising advances for healthcare purposes with specific applications in predicting cancer outcomes. The research investigates how QML can work together with the quantitative imaging analysis techniques in radiomics to improve prediction methods for lung cancer results. The proposed quantum neural network (QNN) model attempts to predict lung cancer patient 5-year survival status of lung cancer patient by radiomic features extracted from CT scan images. Quantum artificial intelligence showed promise in lung cancer prognosis because its evaluation included a balanced accuracy score of 0.71 and ROC-AUC performance metric of 0.71. The current approach has three major challenges due to data biases, restricted quantum hardware capabilities and insufficient evidence to demonstrate the quantum speedup benefits. In this study QML's clinical potential was noted in this research with operational and developmental pathways moving forward.
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 Machine Learning (QML) has emerged as an interdisciplinary field that harnesses the potential of quantum computing to revolutionize classical machine learning paradigms. This paper presents a benchmarking study focused on four quantum classifier algorithms: Quantum Support Vector Classifier (QSVC), Variational Quantum Circuit (VQC), Quantum Neural Network Classifier (QNNC) and Pegasos Quantum Support Vector Classifier (PegasosQSVC). These quantum classifiers hold promise for unlocking quantum advantages in classification tasks. However, a systematic assessment of their performance across diverse datasets is yet to be fully explored. In this research study, we evaluate and compare the classification performance of quantum machine learning classifiers on various datasets (Breast Cancer, Diabetes, and Titanic) and compare them to a classical model. Our findings revealed that PegasosQSVC consistently maintained high performance across all datasets with the highest average accuracy, recall, and F1-score values of 0.81, 0.72, and 0.71, respectively, followed by VQC, followed by VQC, while QSVC and QNNC perform variably. Nevertheless, QSVC was found to balance performance and training time, achieving an average accuracy of 0.81 and a training time of 177 seconds. Overall, the classical model surpassed QML models as it was up to 10% higher in average accuracy.
The merger between Quantum Computing (QC) and Machine Learning (ML) will enable health analytics to be experienced in a way never seen before with unmatched efficiency and power. Conventional methods of healthcare data processing have not been able to work with very big and complex data sets that are related to medical imaging, genomics, and real-time tracking of the patients. Quantum Machine Learning (QML) has an advantage, as it uses new algorithms to work with such multidimensional data with much better accuracy, speed, and predictive power. This paper presents the essential contribution of QML to healthcare analytics, such as supporting diagnosis, to personalized medicine and predictive modelling. This quantum enhanced deep learning models will achieve what is currently being done at the larger scale in the field of medicine at a faster rate a thousand times, through earlier detection of diseases like cancer, Alzheimer disease and heart diseases. In drug discovery, quantum algorithms decrease the simulation of molecular interactions down to the atomic scale, which saves a substantial amount of time and cost of research. Security is, nevertheless, a massive issue since future developments in quantum theory will generate quantum-wise systems that will bring misfortunes to conventional encryption solutions adopted within healthcare systems. The implications of post-quantum cryptography that ensures the strong data privacy and safe patient records in quantum-powered ecosystem are discussed. Scalability obstacles, systems creation, and integration with traditional ML models, however, exist. Real-world applications and future perspectives on the quantum-enhanced healthcare analytics upon which plans have already been laid are presented. Looking ahead, QML will build in the transition of healthcare systems toward analytics frameworks faster, much more precise, and enhanced secure, heralding in the day of precision medicine early diagnostics and a new age for healthcare innovation.
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.