Mixture Markov Model and Hidden Markov Model for Blood Donation Sequence Data

• Main Authors: Chang-Hsuan Chiang, 江長軒
• Other Authors: Shi-Woei Lin
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/896u26

碩士 === 國立臺灣科技大學 === 工業管理系 === 106 === Blood transfusion is essential for certain medical treatments. In recent years, considerable concern has arisen over the issue of how to maintain stable supply of blood components. While a blood center can either hold blood drive campaigns to recruit new donors or encourage regular donors to return to ensure sufficient supply of blood, having a donor donate blood regularly seems to be more valuable than recruiting a new donor. In this study, sequence data which contains blood donation history of donors from 2010 to 2014 were analyzed. In particular, the donors who donate first time in the first half of 2010 were followed up for five years and model-based clustering methods, including mixture Markov model and mixture hidden Markov model, were used to identify the clusters of the donors. After obtaining and interpreting clusters, logistic regression models and random forest models were adopted to investigate how demographic characteristics and the short-term behavior affect a donor's long-term return behavior. Results show that the short-term donation behavior is the most important indicator for predicting a donor's long-term donation behavior. Furthermore, “age” is also significantly associated with a donor's behavior, and those who are older than 40 years old are more likely to return regularly.