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International Journal of Advanced Engineering, Management and Science

Machine Learning and Big Data Analytics in IoT based Blood Bank Supply Chain Management System

( Vol-4,Issue-12,December 2018 )

Author(s): J. Arul Valan, Dr. E. Babu Raj

Total View : 1034
Downloads : 166
Page No: 805-810
ijaems crossref doiDOI: 10.22161/ijaems.4.12.4


Internet of Things(IoT), Big Data, Machine Learning,Blood Bank, Supply Chain Management, Regional Blood Center (RBC), Hospital Blood Bank (HBB), Radio Frequency Identification (RFID).


Blood is a perishable product with uncertainties in both supply and demand and blood stock management is therefore a judicious balance between shortage and wastage.Blood service operations are a key component of the healthcare system. Requirement of blood is increasing gradually due to accidents, surgeries etc. Blood transfusion play an important role in healthcare. The intention of Blood Bank Supply Chain is to demand estimate, inventory management and distribute adequate blood.Internet of Things (IoT) has rehabilitated the traditional e-healthcare system.Big data analytics refers to the process of collecting, organizing and analyzing large sets of data ("big data") to discover patterns and other useful information in a blood bank system. -Big DataAnalyticsandMachine Learning aretwo important areas ofdatascience. A key benefit of Machine Learning is the analysis and learning of massive amount so fun supervised data, making it a valuable tool for Big Data Analytics where raw data is largely unlabeled and un-categorized. With advance research in health sector, there is variety of perishable data available in health care especially in the Blood bank domain. This paper provides review and importance big data technologies and IoT paradigms used in health care sector.

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