Predicting Early Dropouts of an Active and Healthy Ageing App
Vasileios Perifanis Ioanna Michailidi Giorgos Stamatelatos George Drosatos Pavlos S. Efraimidis
In this work, we present a machine learning approach for predicting early
dropouts of an active and healthy ageing app. The presented algorithms have
been submitted to the IFMBE Scientific Challenge 2022, part of IUPESM WC 2022.
We have processed the given database and generated seven datasets. We used
pre-processing techniques to construct classification models that predict the
adherence of users using dynamic and static features. We submitted 11 official
runs and our results show that machine learning algorithms can provide
high-quality adherence predictions. Based on the results, the dynamic features
positively influence a model's classification performance. Due to the
imbalanced nature of the dataset, we employed oversampling methods such as
SMOTE and ADASYN to improve the classification performance. The oversampling
approaches led to a remarkable improvement of 10\%. Our methods won first place
in the IFMBE Scientific Challenge 2022.