Enhance Computing Performance Through Deep Learning and Blockchain Integration for Public Health Problems

Abstract

Blockchain is one of the most phenomenal innovations and becomes an attraction to be widely implemented in public health. Blockchain manages and accesses various data sets distributed across all users. It requires tools that can solve this data processing problem. Deep learning can be used to analyses and process public data efficiently and rapidly. Blockchain and deep learning integration support efforts to address accuracy, latency, centralization, security, and privacy issues. This research proposes the concept and architecture of blockchain and deep learning integration for public health problems. Results show that deep learning is capable of classifying cases with more than 80% accuracy and dengue fever cases forecasting with average RMSE 10,39