Abstract
Health Monitoring System (HMS) is an integrated system including the sub-systems of health data gathering through sensors, health data analysis, and patient-doctor realtime communication. HMS allows patients to get medical care remotely and doctors to provide real-time information to patients. The system not only reduces patients' time-cost but also increases the quality of medical care. To solve the security problem of centralized management of data, blockchain technology has been introduced, as it contains the properties of immutability, transparency, and distribution. To take the advantage of blockchain network structure, this paper proposed a system framework integrated with decentralized machine learning aiming to improve the system performance in terms of throughput and model accuracy. It is a combination of the Hyperledger Fabric network and ModelChain model training method, where Hyperledger Fabric allows users to be grouped and managed in the form of organizations, and ModelChain uses the characteristics of grouped users to explore more value of information. This paper proposes the scheme to allow access control on a Hyperledger Fabric system and the algorithm to implement ModelChain on a Hyperledger Fabric network. Furthermore, the system is built and measured by tools, such as Hyperledger Caliper, Docker, and Weka, and is evaluated in terms of system throughput and accuracy.
| Original language | English |
|---|---|
| Pages (from-to) | 1-7 |
| Number of pages | 7 |
| Journal | IEEE Communications Magazine |
| DOIs | |
| Publication status | Accepted/In press - 2023 |
Keywords
- Blockchains
- Data models
- Fabrics
- Hospitals
- Machine learning
- Organizations
- Peer-to-peer computing
ASJC Scopus subject areas
- Computer Science Applications
- Computer Networks and Communications
- Electrical and Electronic Engineering