  Medication non-adherence is a widespread problem affecting over 50% of people
who have chronic illness and need chronic treatment. Non-adherence exacerbates
health risks and drives significant increases in treatment costs. In order to
address these challenges, the importance of predicting patients' adherence has
been recognised. In other words, it is important to improve the efficiency of
interventions of the current healthcare system by prioritizing resources to the
patients who are most likely to be non-adherent. Our objective in this work is
to make predictions regarding individual patients' behaviour in terms of taking
their medication on time during their next scheduled medication opportunity. We
do this by leveraging a number of machine learning models. In particular, we
demonstrate the use of a connected IoT device; a "Smart Sharps Bin", invented
by HealthBeacon Ltd.; to monitor and track injection disposal of patients in
their home environment. Using extensive data collected from these devices, five
machine learning models, namely Extra Trees Classifier, Random Forest, XGBoost,
Gradient Boosting and Multilayer Perception were trained and evaluated on a
large dataset comprising 165,223 historic injection disposal records collected
from 5,915 HealthBeacon units over the course of 3 years. The testing work was
conducted on real-time data generated by the smart device over a time period
after the model training was complete, i.e. true future data. The proposed
machine learning approach demonstrated very good predictive performance
exhibiting an Area Under the Receiver Operating Characteristic Curve (ROC AUC)
of 0.86.
