  Due to the chronic nature of diabetes, patient self-care factors play an
important role in any treatment plan. In order to understand the behaviour of
patients in response to medical advice on self-care, clinicians often conduct
cross-sectional surveys. When analysing the survey data, statistical machine
learning methods can potentially provide additional insight into the data
either through deeper understanding of the patterns present or making
information available to clinicians in an intuitive manner. In this study, we
use self-organising maps (SOMs) to visualise the responses of patients who
share similar responses to survey questions, with the goal of helping
clinicians understand how patients are managing their treatment and where
action should be taken. The principle behavioural patterns revealed through
this are that: patients who take the correct dose of insulin also tend to take
their injections at the correct time, patients who eat on time also tend to
correctly manage their food portions and patients who check their blood glucose
with a monitor also tend to adjust their insulin dosage and carry snacks to
counter low blood glucose. The identification of these positive behavioural
patterns can also help to inform treatment by exploiting their negative
corollaries.
