Open AccessDELVING INTO BLOOD TRANSFUSIONS DATA THROUGH DATA MINING: A STUDY OF MAIZBHANDARI SHAH EMDADIA BLOOD DONORS GROUP TO SELECT VOLUNTEER BLOOD DONORS EFFICIENTLY
Author(s) -
Syed Irfanul Hoque,
Md. Min-ha-zul Abedin,
Mihir Chowdhury
Publication year - 2021
Publication title -
american international journal of multidisciplinary scientific research
Language(s) - English
Resource type - Journals
eISSN - 2638-1249
pISSN - 2638-1273
DOI - 10.46281/aijmsr.v10i1.1308
Subject(s) - volunteer , blood transfusion , blood donor , medicine , descriptive statistics , descriptive research , volunteer work , medical emergency , surgery , immunology , statistics , biology , public relations , mathematics , political science , agronomy
The demand for blood transfusion is rising gradually. Therefore, volunteer blood donors are needed to save the lives of patients. There are lot of volunteer donors in every society. But, the main problem people face is finding such donors at the right time. To locate potential blood donors, we collected data from the Maizbhandari Shah Emdadia blood donors’ group, which consists of 700 active volunteer blood donors. This study aims to choose potential volunteer donors efficiently at the emergency time from the Maizbhandari Shah Emdadia blood donors’ group based on their past data. We have developed two models, namely descriptive and predictive models, using data mining techniques. The descriptive model analyzes data patterns and explores the donors’ behaviour. A data mining clustering algorithm was used to develop the descriptive model. The underlying factors of donors' intention to donate blood were identified and evaluated using this descriptive model. These factors were then utilized to develop the predictive model, which in turn assists to predict whether a donor will donate blood or not during an emergency. The findings of these two models will assist the clinical experts in locating potential volunteer blood donors within the shortest period and thus save valuable lives.