期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2019
卷号：10
期号：8
DOI：10.14569/IJACSA.2019.0100816
出版社：Science and Information Society (SAI)
摘要：Since blood centers in most countries typically rely on volunteer donors to meet the hospitals' needs, donor retention is critical for blood banks. Identifying regular donors is critical for the advance planning of blood banks to guarantee a stable blood supply. In this research, donors' data was collected from a Saudi blood bank from 2017 to 2018. Machine learning algorithms such as logistic regression (LG), random forest (RF) and support vector classifier (SVC) were applied to develop and evaluate models for classifying blood donors as return and non-return donors. The natural imbalance of the donors' distribution required extra attention and considerations to produce classifiers with good performance. Thus, over-SMOTE sampling was tested. Experiments of different classifiers showed very similar performance results. In addition to the donors return classification, a time series analysis on the donors dataset was also considered to find any seasonal variations that could be captured and delivered to blood banks for better planning and decision making. After aggregating the donation count by month, results showed that the number of donations each year was stable except for two discovered drops in June and September, which for the two observed years coincided with two religious periods: Fasting and Performing Hajj.
关键词：Classification; machine learning; time series analysis; blood donation
