A Blockchain Based Data Sharing System for Health Care Applications

Abstract

Researchers recently were battling to produce an ever-more effective prediction model. Studies have found that when the artificial intelligence (AI) model is trained with a wealth of information, it performs better and generalizes better. Research institutions, testing labs, hospitals, and other organizations can share their information and work together to improve the training algorithm. All companies wanted to respect the confidentiality of their data, but they still want effective and precise teaching methods for a spectrum of uses. Regarding the ethical and regulatory concerns about medical data privacy, data sharing among numerous organizations is limited. In health care systems,we describe a unique solution that integrates locally taught AI from over blockchain for enhancing disease prediction by addressing the identified gap, while maintaining anonymity and allowing data exchange. Using the information to ensure privacy poses a number of issues. This study provides a Modified Needleman-Wunsch algorithm (MNWA) for data sharing. The Artificial Neural Network (ANN) model is used for trust-enabled smart contracts. Python software performs the implementation work. A detailed experimental survey was performed to test the importance of our proposed strategy for effective early prediction and diagnosis. The proposed model's extensive experiments demonstrate a proposed method may detect ailments and boost efficiency.