Department of Electronics and Computer Science, Shri Ramdeobaba College of Engineering and Management, Ramdeobaba University, Nagpur, Maharashtra, India.
Leveraging cutting-edge technology like blockchain and machine intelligence, smart healthcare systems have emerged as a potential strategy for enhancing healthcare services. In order to secure health data, this study offers a unique design and analysis of a smart healthcare system that applies blockchain technique and the paillier homomorphic encryption algorithm in addition to a machine learning algorithm to detect cardiological disease. The suggested method seeks to solve the problems with predictive analytics and safe health data exchange in the medical field. Sensitive information is encrypted during transmission and storage using the Paillier Homomorphic Encryption technique, guaranteeing its confidentiality. By providing traceability and accountability in data access and sharing, blockchain technology is used to construct a safe and transparent record of health transactions. In addition, a machine learning algorithm is used to forecast cardiac illness based on the encrypted data, giving medical practitioners insightful information to help them make judgments. The integration of these technologies and their advantages in improving healthcare services are highlighted in the discussion of the proposed scheme's constructional and operational specification section. Simulation experiments are used to assess the suggested method’s efficiency and reflect its efficacy in terms of data security, detection accurateness, and computing proficiency. Comparing the integrated approach to conventional approaches, the results demonstrate a considerable improvement in prediction accuracy and security of health data. To sum up, the suggested smart healthcare system provides a thorough approach to guaranteeing the security of patient data and enhancing predictive analytics in the medical field. Machine learning, blockchain technology, and Paillier homomorphic encryption are all integrated into it, which shows promise for improving healthcare services and developing the field of smart healthcare systems.
Keywords
Blockchain Technology, Machine Learning, Homomorphic Encryption, Accuracy, Healthcare System, Internet of Medical Things (IoMT).
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Subhra Prosun Paul
Department of Computer Science and Engineering, Uttara University, Dhaka, Bangladesh.
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Cite this article
Subhra Prosun Paul, Sreenivasu S V N, Md. Shafikul Islam, Raghunath B, Kanchan Dhote and Vetrithangam D, “Integrating Homomorphic Encryption with Blockchain Technology for Machine Learning Applications”, Journal of Machine and Computing. doi: 10.53759/7669/jmc202505031.
