IMPLEMENTATION TO IMPROVISE BLOOD DONATION PROCESS USING DATA MINING TECHNIQUES

Authors:

Hrushikesh Panchabudhhe, Komal Palkar, Aditee Padgilwar, Krishna Rathod, Shubham Rathod, Prof. Shital Tatale

Page No: 1310-1315

Abstract:

With an ever-increasing demand for blood inventories worldwide, there is an immense need to insure a safe and sufficient supply of blood products. However, recruiting and retaining blood donors remain key challenges for blood agencies. In an attempt to this problem, researchers have identified the range of socio-demographic, organizational, and psychological factors that influence people's willingness to donate blood. While past research has largely focused on donor conscription, in particular enumeration variables related to blood donation behavior, the issues of donor maintenance have become increasingly important. A growing number of studies have also tautness the part of cerebral factors in explaining, predicting, and prognosticating blood donation behavior. Although there is a superimposed between factors that forecast the initiation and the maintenance of blood donation behavior, it is recommended that changes in motivation and the development of self-identity as a blood donor are crucial for understanding the process whereby first time donors become repeat donors. We are implementing machine learning algorithms our proposed random forest algorithm gives high proficiency and accuracy.

Description:

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Volume & Issue

Volume-12,Issue-4

Keywords

Blood, Demographic, Machine Learning, Random Forest, Data Mining