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.

翻译：本文提出了一种用于预测主动健康老龄化应用早期退出的机器学习方法。所提算法已提交至2022年IFMBE科学挑战赛（作为IUPESM 2022世界大会的一部分）。我们对给定数据库进行了处理，生成了七个数据集。采用预处理技术构建分类模型，利用动态特征与静态特征预测用户依从性。共提交11次正式运行结果，研究表明机器学习算法能够提供高质量的依从性预测。基于实验结果，动态特征对模型分类性能具有积极影响。针对数据集存在的类别不平衡问题，采用SMOTE和ADASYN等过采样方法提升分类性能，过采样策略使性能显著提升10%。本方法荣获2022年IFMBE科学挑战赛第一名。