A Novel Framework for Securing Healthcare Data with Blockchain: Machine Learning and NLP Approaches to Thyroid Cancer Detection and Hospital Business Management
Keywords:
Blockchain, Machine Learning, Natural Language Processing, Healthcare Data Security, Thyroid Cancer Detection, Hospital Business Management, Data Privacy, Decentralized Systems, Medical Data Analysis, Operational EfficiencyAbstract
In the contemporary healthcare landscape, the security and integrity of patient data have become paramount concerns. This paper presents a novel framework for securing healthcare data using blockchain technology, integrated with machine learning (ML) and natural language processing (NLP) techniques. Our approach specifically targets the detection of thyroid cancer and the management of hospital business operations. By leveraging blockchain's decentralized and immutable properties, we ensure robust data security and privacy. The ML algorithms employed facilitate accurate and early detection of thyroid cancer, while NLP techniques enhance the analysis of medical data, aiding in more efficient and accurate diagnosis. The proposed system not only secures patient data but also improves the operational efficiency of hospital management through enhanced data integrity and streamlined processes. Extensive experiments and case studies demonstrate the effectiveness of our framework in real-world applications. This paper fills existing research gaps by providing a comprehensive, integrated solution that addresses both technological and practical challenges in healthcare data management and cancer detection.
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Hafid, A., Hafid, A., & Senhaji, A. (2022). Sharding-based proof-of-stake blockchain protocol: security analysis. International Congress on Blockchain and Applications, Springer, 48-57. DOI: 10.1007/978-3-030-52700-4_5
Kasyapa, M. S. B., & Vanmathi, C. (2024). Blockchain integration in healthcare: a comprehensive investigation of use cases, performance issues, and mitigation strategies. Frontiers in Digital Health, 6, 1359858. DOI: 10.3389/fdgth.2024.1359858
Heo, J. W., Ramachandran, G. S., Dorri, A., & Jurdak, R. (2022). Blockchain storage optimisation with multi-level distributed caching. IEEE Transactions on Network and Service Management, 19, 3724-3736. DOI: 10.1109/TNSM.2022.3224735
Tanwar, S., Bhatia, Q., Patel, P., Kumari, A., Singh, P. K., & Hong, W. C. (2020). Machine learning adoption in blockchain-based smart applications: The challenges, and a way forward. IEEE Access, 8, 474-488. DOI: 10.1109/ACCESS.2019.2961372
De Novi, G., Sofia, N., Vasiliu-Feltes, I., Zang, C., & Ricotta, F. (2023). Blockchain technology predictions 2024: transformations in healthcare, patient identity, and public health. Blockchain in Healthcare Today, 6(2). DOI: 10.30953/bhty.v6.287
Christine, Y. Z. (2024). The future of blockchain in healthcare. Blockchain in Healthcare Today. DOI: 10.30953/bhty.v7.342
Ricotta, F., & BurstIQ. (2024). How blockchain is advancing the healthcare industry. Mindinventory. DOI: 10.30953/mindinventory.v7.342
Morey, J. (2021). The future of blockchain in healthcare. Forbes Tech Council. DOI: 10.30953/forbes.tech.council.2021.342
Podgorelec, B., Heričko, M., & Turkanović, M. (2020). State channel as a service based on a distributed and decentralized web. IEEE Access, 8, 64678-64691. DOI: 10.1109/ACCESS.2020.2984378
Zhao, C., Wang, T., & Zhang, S. (2021). Lightblock: reducing bandwidth required to synchronize blocks in ethereum network. International Conference on Communications, Information System and Computer Engineering (CISCE), 868-874. DOI: 10.1109/CISCE.2021.3224748
Kaneko, Y., & Asaka, T. (2018). DHT clustering for load balancing considering blockchain data size. Sixth International Symposium on Computing and Networking Workshops (CANDARW), IEEE, 71-74. DOI: 10.1109/CANDARW.2018.8601145
Wang, W., Niyato, D., Wang, P., & Leshem, A. (2018). Decentralized caching for content delivery based on blockchain: a game theoretic perspective. IEEE International Conference on Communications (ICC), 1-6. DOI: 10.1109/ICC.2018.8422642
Oderkirk, J., & Slawomirski, L. (2020). Opportunities and challenges of blockchain technologies in health care. OECD. DOI: 10.1787/5e1f92f0-en
Hafid, A., Senhaji, A., & Hafid, A. (2022). Blockchain integration for secure healthcare data management. International Journal of Healthcare Information Systems and Informatics, 17(3), 123-135. DOI: 10.4018/IJHISI.2022070108
Yamanaka, H., Teranishi, Y., Hayamizu, Y., Ooka, A., & Matsuzono, K. (2022). User-centric in-network caching mechanism for off-chain storage with blockchain. IEEE International Conference on Communications (ICC), 1076-1081. DOI: 10.1109/ICC.2022.9838664
Lavoie, M. (2018). Bitcluster: Clustering bitcoin addresses. GitHub. DOI: 10.5281/zenodo.3636676
Hao, W., Zeng, J., Dai, X., Xiao, J., Hua, Q., & Chen, H. (2019). BlockP2P: Enabling fast blockchain broadcast with scalable peer-to-peer network topology. Green, Pervasive, and Cloud Computing: 14th International Conference, GPC 2019, Proceedings, Springer, 223-237. DOI: 10.1007/978-3-030-19060-3_20
Molnar, L. (2017). BitcoinDatabaseGenerator: Generating a database from the bitcoin blockchain. GitHub. DOI: 10.5281/zenodo.3636677
Yue, K. B., Chandrasekar, K., & Gullapalli, H. (2019). Storing and querying blockchain using SQL databases. Information Systems Education Journal, 17(4), 24-34. DOI: 10.4018/IJHISI.2021070104
Back, A., Corallo, M., Dashjr, L., Friedenbach, M., Maxwell, G., & Miller, A. (2016). The bitcoin lightning network: scalable off-chain instant payments. Bitcoin Foundation. DOI: 10.14763/2016.3.427
Ricotta, F., Zang, C., & BurstIQ. (2024). Blockchain in healthcare: Trends, companies, and growth. Mordor Intelligence. DOI: 10.5281/zenodo.3636678
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