Enhancing the Human Health Status Prediction: The ATHLOS Project

Anagnostou, P., Tasoulis, S., Vrahatis, A. G., Georgakopoulos, S., Prina, M., Ayuso-Mateos, J. L., Bickenbach, J., Bayes-Marin, I., et al. (2021). Enhancing the Human Health Status Prediction: The ATHLOS Project. Applied Artificial Intelligence 1-23. 10.1080/08839514.2021.1935591.

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Project: Ageing Trajectories of Health: Longitudinal Opportunities and Synergies (ATHLOS, H2020 635316)


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.

Item Type: Article
Research Programs: Population and Just Societies (POPJUS)
Population and Just Societies (POPJUS) > Social Cohesion, Health, and Wellbeing (SHAW)
Depositing User: Luke Kirwan
Date Deposited: 05 Jul 2021 09:06
Last Modified: 27 Aug 2021 17:34
URI: https://pure.iiasa.ac.at/17292

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