Smart Healthcare: Blockchain-Driven Patient Risk Classification with Machine Learning - Jyoti Sunil More (International Journal of Intelligent Systems and Applications in Engineering, 2024)
The development of any nation's economy depends largely on having an efficient healthcare system. This article investigates the architecture of a framework for patient risk classification in a smart healthcare system which combines blockchain technology with machine learning. The framework seeks to transform healthcare delivery by utilizing machine learning algorithms' predictive powers in conjunction with blockchain's transparency, immutability, and decentralized data storage. This study clarifies the potential to improve patient outcomes through more precise and customized risk assessments by thoroughly analyzing the opportunities and challenges. Data security, interoperability, regulatory compliance, and technological scalability are important factors to take into account. The findings of this study provide encouraging paths for enhancing patient care and public health outcomes globally, since interdisciplinary cooperation and continuous innovation propel improvements in healthcare technology. DOI: https://doi.org/10.17762/ijisae.v12i3.6105