TY - JOUR
T1 - Enhancing the Human Health Status Prediction
T2 - The ATHLOS Project
AU - Anagnostou, P.
AU - Tasoulis, S.
AU - Vrahatis, A. G.
AU - Georgakopoulos, S.
AU - Prina, M.
AU - Ayuso-Mateos, J. L.
AU - Bickenbach, J.
AU - Bayes-Marin, I.
AU - Caballero, F. F.
AU - Egea-Cortés, L.
AU - García-Esquinas, E.
AU - Leonardi, M.
AU - Scherbov, S.
AU - Tamosiunas, A.
AU - Galas, A.
AU - Haro, J. M.
AU - Sanchez-Niubo, A.
AU - Plagianakos, V.
AU - Panagiotakos, D.
N1 - Funding Information:
This work is supported by the ATHLOS (Ageing Trajectories of Health: Longitudinal Opportunities and Synergies) project, funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement number 635316.
Publisher Copyright:
© 2021 Taylor & Francis.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85108244204&partnerID=8YFLogxK
U2 - 10.1080/08839514.2021.1935591
DO - 10.1080/08839514.2021.1935591
M3 - Article
AN - SCOPUS:85108244204
SN - 0883-9514
VL - 35
SP - 834
EP - 856
JO - Applied Artificial Intelligence
JF - Applied Artificial Intelligence
IS - 11
ER -
