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In this work, we present a machine learning approach for predicting earlydropouts of an active and healthy ageing app. The presented algorithms havebeen submitted to the IFMBE Scientific Challenge 2022, part of IUPESM WC 2022.We have processed the given database and generated seven datasets. We usedpre-processing techniques to construct classification models that predict theadherence of users using dynamic and static features. We submitted 11 officialruns and our results show that machine learning algorithms can providehigh-quality adherence predictions. Based on the results, the dynamic featurespositively influence a model's classification performance. Due to theimbalanced nature of the dataset, we employed oversampling methods such asSMOTE and ADASYN to improve the classification performance. The oversamplingapproaches led to a remarkable improvement of 10\%. Our methods won first placein the IFMBE Scientific Challenge 2022.

Predicting Early Dropouts of an Active and Healthy Ageing App