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@article{176447,
        author = {R B Maria Sofia and Dr R Parameswari},
        title = {Cloud computing-based framework for heart disease classification using quantum machine learning approach},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {7347-7350},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=176447},
        abstract = {heart disease remains one of the leading causes of mortality worldwide, emphasizing the urgent need for accurate and timely diagnosis. Traditional machine learning models have been widely adopted for heart disease classification; however, they often encounter limitations in computational speed and predictive performance when dealing with large, complex datasets. In this study, we propose a cloud computing-based framework integrated with a quantum machine learning (QML) approach to enhance the classification of heart disease. By leveraging the immense computational power and scalability of cloud platforms, our framework efficiently manages and processes medical data, ensuring accessibility and real-time analytics. The quantum machine learning models, utilizing quantum-enhanced feature spaces and parallelism, offer improved accuracy and faster convergence compared to classical methods. Experimental results demonstrate that the proposed system outperforms conventional machine learning algorithms in terms of classification accuracy, precision, recall, and computational efficiency. This hybrid architecture not only supports scalable and secure healthcare analytics but also paves the way for the adoption of quantum computing in real-world medical applications. Future work will focus on optimizing quantum circuits and expanding the framework to support multi-disease classification tasks.},
        keywords = {computational speed, Leveraging, Quantum Machine learning,},
        month = {April},
        }