A Novel Framework for Securing Healthcare Data with Blockchain: Machine Learning and NLP Approaches to Thyroid Cancer Detection and Hospital Business Management

Authors

  • Gaganjot Kaur, Manisha M. More, Mini Thomas, Chhavi Sharma, Ajay Kumar, Chaitali Bhattacharya, Kumar P.

Keywords:

Blockchain, Machine Learning, Natural Language Processing, Healthcare Data Security, Thyroid Cancer Detection, Hospital Business Management, Data Privacy, Decentralized Systems, Medical Data Analysis, Operational Efficiency

Abstract

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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References

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Published

10.07.2024

How to Cite

Gaganjot Kaur. (2024). A Novel Framework for Securing Healthcare Data with Blockchain: Machine Learning and NLP Approaches to Thyroid Cancer Detection and Hospital Business Management. International Journal of Intelligent Systems and Applications in Engineering, 12(23s), 140–149. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/6605

Issue

Section

Research Article