Blood Transfusion Service Center

Donated on 10/2/2008

Data taken from the Blood Transfusion Service Center in Hsin-Chu City in Taiwan -- this is a classification problem.

Dataset Characteristics

Multivariate

Subject Area

Business

Associated Tasks

Classification

Feature Type

Real

# Instances

748

# Features

-

Dataset Information

Additional Information

To demonstrate the RFMTC marketing model (a modified version of RFM), this study adopted the donor database of Blood Transfusion Service Center in Hsin-Chu City in Taiwan. The center passes their blood transfusion service bus to one university in Hsin-Chu City to gather blood donated about every three months. To build a FRMTC model, we selected 748 donors at random from the donor database. These 748 donor data, each one included R (Recency - months since last donation), F (Frequency - total number of donation), M (Monetary - total blood donated in c.c.), T (Time - months since first donation), and a binary variable representing whether he/she donated blood in March 2007 (1 stand for donating blood; 0 stands for not donating blood).

Has Missing Values?

No

Variables Table

Variable NameRoleTypeDemographicDescriptionUnitsMissing Values
no
no
no
no
no

0 to 5 of 5

Additional Variable Information

Given is the variable name, variable type, the measurement unit and a brief description. The "Blood Transfusion Service Center" is a classification problem. The order of this listing corresponds to the order of numerals along the rows of the database. R (Recency - months since last donation), F (Frequency - total number of donation), M (Monetary - total blood donated in c.c.), T (Time - months since first donation), and a binary variable representing whether he/she donated blood in March 2007 (1 stand for donating blood; 0 stands for not donating blood). Table 1 shows the descriptive statistics of the data. We selected 500 data at random as the training set, and the rest 248 as the testing set. Table 1. Descriptive statistics of the data Variable Data Type Measurement Description min max mean std Recency quantitative Months Input 0.03 74.4 9.74 8.07 Frequency quantitative Times Input 1 50 5.51 5.84 Monetary quantitative c.c. blood Input 250 12500 1378.68 1459.83 Time quantitative Months Input 2.27 98.3 34.42 24.32 Whether he/she donated blood in March 2007 binary 1=yes 0=no Output 0 1 1 (24%) 0 (76%)

Papers Citing this Dataset

Batch Active Learning Using Determinantal Point Processes

By Erdem Biyik, Kenneth Wang, Nima Anari, Dorsa Sadigh. 2019

Published in ArXiv.

A Similarity Classifier with Bonferroni Mean Operators

By Onesfole Kurama, Pasi Luukka, Mikael Collan. 2016

Published in Adv. Fuzzy Systems.

Kernel-Based Just-In-Time Learning for Passing Expectation Propagation Messages

By Wittawat Jitkrittum, Arthur Gretton, Nicolas Heess, S. Eslami, Balaji Lakshminarayanan, Dino Sejdinovic, Zolt'an Szab'o. 2015

Published in ArXiv.

© 2010 Science Publications Application of CART Algorithm in Blood Donors Classification

By T. Santhanam, Shyam Sundaram. 2010

Published in Journal of Computer Science.

0 to 4 of 4

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4 citations
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Creators

I-Cheng Yeh

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