Blood Transfusion System Using Data Mining Techniques and GRA

Agarwal, Kanishk; Gupta, Malaika; Gupta, Khushboo; Khan, Arshad; Nallakaruppan, M. K.

doi:10.1109/icaccs.2019.8728401

Cited by 3 publications

(2 citation statements)

References 10 publications

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“…Significance of multidimensional data analysis in the healthcare sector and the benefits which it brings about have already been recognized in other countries. Works are being published worldwide to demonstrate the potential use of advanced IT tools in the healthcare sector as well as tools for analysis of large and diverse data sets (Big Data and Data Mining) [16][17][18][19]. Sooner or later Poland will be following the same path and proceeding in the same direction.…”

Section: Discussionmentioning

confidence: 99%

Retrospective analysis of selected aspects of public blood transfusion service as a starting point for assessment of the status of transfusion medicine in Poland Part 1: Demographic characteristics of the donor population reporting for blood donation

Mikołowska

Antoniewicz‐Papis

2020

Journal of Transfusion Medicine

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Background: The ongoing demographic changes in Poland affect the number of donors and donations with direct impact on provision of blood and blood components for transfusion. With predicted population-decline, the number of potential donors is likely to decrease while the demand for blood is bound to increase as more people will require blood therapy. The structure of the Polish donor population reporting for blood donation is not uniform, moreover it has undergone additional changes within the study period. So it seems justified to perform in-depth analysis of the dynamics of changes in the number and structure of donor group as well as determine the direction and intensity of these changes in spatial and temporal terms. Identification of the possible causes of change is also important. Aim: presentation of the structure characteristics of the donor group who reported at the Polish regional blood transfusion centers (RBTCs) in the period 2005-2017 with the intention of donating blood/ blood components. Materials and methods: Analyses of available data-sets -annual reports from 21 Polish RBTCs forwarded to the IHTM. The following tools were used for statistical analysis: Micro-

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Section: Discussionmentioning

confidence: 99%

Retrospective analysis of selected aspects of public blood transfusion service as a starting point for assessment of the status of transfusion medicine in Poland Part 1: Demographic characteristics of the donor population reporting for blood donation

Mikołowska

Antoniewicz‐Papis

2020

Journal of Transfusion Medicine

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“…(Bhardwaj, 2012). Agarwal et al (2019) suggested a blood transfusion system using various data mining algorithms such as Random Forest, Decision Tree, and Logistic Regression. They found that the Random Forest algorithm shows the highest efficiency in predicting whether a person will donate blood or not, based on the past data of donation.…”

Section: Literature Review Data Mining In Blood Donationmentioning

confidence: 99%

Delving Into Blood Transfusions Data Through Data Mining: A Study of Maizbhandari Shah Emdadia Blood Donors Group to Select Volunteer Blood Donors Efficiently

2021

aijmsr

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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.

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Data Integration Using Data Mining and SMS Reminder for Automation of Blood Donation

Izadeen

Abdulazeez

Zeebaree

et al. 2021

2021 IEEE International Conference on Automatic Control &Amp; Intelligent Systems (I2CACIS)

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Blood Transfusion System Using Data Mining Techniques and GRA

Cited by 3 publications

References 10 publications

Retrospective analysis of selected aspects of public blood transfusion service as a starting point for assessment of the status of transfusion medicine in Poland Part 1: Demographic characteristics of the donor population reporting for blood donation

Retrospective analysis of selected aspects of public blood transfusion service as a starting point for assessment of the status of transfusion medicine in Poland Part 1: Demographic characteristics of the donor population reporting for blood donation

Delving Into Blood Transfusions Data Through Data Mining: A Study of Maizbhandari Shah Emdadia Blood Donors Group to Select Volunteer Blood Donors Efficiently

Data Integration Using Data Mining and SMS Reminder for Automation of Blood Donation

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