ABSTRACT

Preventive healthcare is a crucial pillar of health as it contributes to staying healthy and having immediate treatment when needed. Mining knowledge from longitudinal studies has the potential to significantly contribute to the improvement of preventive healthcare. Unfortunately, data originated from such studies are characterized by high complexity, huge volume, and a plethora of missing values. Machine Learning, Data Mining and Data Imputation models are utilized a part of solving these challenges, respectively. Toward this direction, we focus on the development of a complete methodology for the ATHLOS Project – funded by the European Union’s Horizon 2020 Research and Innovation Program, which aims to achieve a better interpretation of the impact of aging on health.

The inherent complexity of the provided dataset lies in the fact that the project includes 15 independent European and international longitudinal studies of aging. In this work, we mainly focus on the HealthStatus (HS) score, an index that estimates the human status of health, aiming to examine the effect of various data imputation models to the prediction power of classification and regression models. Our results are promising, indicating the critical importance of data imputation in enhancing preventive medicine’s crucial role.

摘要

预防保健是健康的重要支柱，因为它有助于保持健康并在需要时立即接受治疗。从纵向研究中挖掘知识有可能为改善预防保健做出重大贡献。不幸的是，源自此类研究的数据具有复杂性高、体积大和缺失值过多的特点。机器学习、数据挖掘和数据插补模型分别用于解决这些挑战。朝着这个方向，我们专注于为 ATHLOS 项目开发一套完整的方法论——由欧盟地平线 2020 研究与创新计划资助，旨在更好地解释老龄化对健康的影响。

所提供数据集的内在复杂性在于该项目包括 15 项独立的欧洲和国际老龄化纵向研究。在这项工作中，我们主要关注 HealthStatus (HS) 分数，这是一个估计人类健康状况的指标，旨在检查各种数据插补模型对分类和回归模型的预测能力的影响。我们的结果很有希望，表明数据插补在增强预防医学的关键作用方面的重要性。