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Characterization of Behavioural Patterns of Portuguese Blood Donors using Supervised and Unsupervised Learning
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Orientador(es)
Resumo(s)
Blood donations are essential to save innumerous lives on a global scale on a daily basis. Without blood
donations, many medical procedures cannot take place. Thus, the study of what motivates blood donors
to donate and how they behave is important to ensure a stable and safe blood supply.
Several studies tried to understand the most important factors for blood donor return, by using mainly
logistic regression models. Those studies identified several donor demographic characteristics as impor tant factors to describe donors’ future behaviour. However, in this dissertation it is argued that if models
have a poor performance in the task for which they are trained for, the conclusions taken from them may
be erroneous. Thus, this dissertation presents a contribution for the study of understanding blood donor
behaviour by using the most recent machine learning, evaluation and interpretability techniques.
In this dissertation, several machine learning experiments are implemented aiming to predict blood
donors return one year following a given donation, gaining insights about blood donors future behaviour
and which factors influence it the most. Primarily, the blood donations dataset is split according to several
geographic characteristics. Each segment is further split into blood donations from new and experienced
donors (i.e. those who donated more than once). For experienced donors several features regarding
their past behaviour are computed. Finally, different machine learning models are trained on top of each
segment.
Our results suggest that donor’s demographics, as well as features regarding the donation, are not
enough to predict donor return. As such, it is not possible to estimate the impact that donor’s demo graphics have on donor’s future behaviour. However, models trained over experienced donors performed
significantly better than those trained over new donors data, due to the impact of past behaviour features.
However, even with past behaviour features the machine learning models do not achieve outstanding
scores in predicting donor future behaviour, and, as such, this work demonstrates that both demographics
and past behaviour features are insufficient to accurately explain future behaviour.
Descrição
Tese de mestrado, Ciência de Dados, Universidade de Lisboa, Faculdade de Ciências, 2022
Palavras-chave
Dador de Sangue doação de sangue aprendizagem automática Teses de mestrado - 2022