Novel Approach to Blood Supply System Using Machine Learning and Blockchain Technology

Sweetline Priya, E. and Priya, R. (2024) Novel Approach to Blood Supply System Using Machine Learning and Blockchain Technology. In: Evolution in Signal Processing and Telecommunication Networks. Springer, pp. 361-378.

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Abstract

Despite the enormous improvement in technology, blood bank systems are either manual or there is no centralised system to maintain the same. A number of problems arise as a result, such as the insufficient blood quality management makes it difficult to track the elements of blood from their collection to consumption, the need to keep blood at a specified temperature, and the potential for transfusion-oriented infections like Malaria, AIDS, Syphilis and Hepatitis B&C. Blood is limited in certain places, yet because of its short shelf life, blood is squandered in other places. Blockchain technology (BCT) is a suitable application in the blood donation domain for management of supply chain due to the traceable and immutable nature of the data maintained in blockchain. Various cryptocurrency-based and non-cryptocurrency-based apps nowadays opt for BCT. The purpose of this study is to outline the architecture for a two-module block chain technology (BCT) cum machine learning (ML) supported blood donation management system. For the efficient administration of blood among various actors of the blood supply system (BSS) such as blood donors, blood-bank, medical centre and patients, the first part is proposed based on BCT. For the finding of blood transfusion transmissible diseases shortly TTI, the second module is proposed based on machine learning. In this paper, the first module of the suggested architecture, which entails collecting blood and storing it under blockchain following determining his eligibility for donation, is implemented. To implement the same, the Hyperledger Fabric tool, a permissioned open-source BC platform with distributed ledger is used in the suggested paradigm. The system users will find it simple to track the blood using the suggested model. TTI is checked twice on the blood that was obtained. One has a blood testing facility, while the other uses a planned ML-based detection. As a result, the blood recipient may reliably get and use the blood since the blood supply is transparent from its collection till consumption. Additionally, donors can receive updates on the status of their blood if used. This helps to motivate them to give blood again in the future by the system.

Item Type: Book Section
Subjects: Computer Science Engineering > Machine Learning
Divisions: Computer Applications
Depositing User: Mr IR Admin
Date Deposited: 08 Oct 2024 11:48
Last Modified: 08 Oct 2024 11:48
URI: https://ir.vistas.ac.in/id/eprint/9502

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