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A divisive hierarchical clustering methodology for enhancing the ensemble prediction power in large scale population studies: the ATHLOS project

Lookup NU author(s): Professor Matthew Prina

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Abstract

© 2022, The Author(s), under exclusive licence to Springer Nature Switzerland AG.The ATHLOS cohort is composed of several harmonized datasets of international groups related to health and aging. As a result, the Healthy Aging index has been constructed based on a selection of variables from 16 individual studies. In this paper, we consider additional variables found in ATHLOS and investigate their utilization for predicting the Healthy Aging index. For this purpose, motivated by the volume and diversity of the dataset, we focus our attention upon data clustering, where unsupervised learning is utilized to enhance prediction power. Thus we show the predictive utility of exploiting hidden data structures. In addition, we demonstrate that imposed computation bottlenecks can be surpassed when using appropriate hierarchical clustering, within a clustering for ensemble classification scheme, while retaining prediction benefits. We propose a complete methodology that is evaluated against baseline methods and the original concept. The results are very encouraging suggesting further developments in this direction along with applications in tasks with similar characteristics. A straightforward open source implementation for the R project is also provided (https://github.com/Petros-Barmpas/HCEP).


Publication metadata

Author(s): Barmpas P, Tasoulis S, Vrahatis AG, Georgakopoulos SV, Anagnostou P, Prina M, Ayuso-Mateos JL, Bickenbach J, Bayes I, Bobak M, Caballero FF, Chatterji S, Egea-Cortes L, Garcia-Esquinas E, Leonardi M, Koskinen S, Koupil I, Pajak A, Prince M, Sanderson W, Scherbov S, Tamosiunas A, Galas A, Haro JM, Sanchez-Niubo A, Plagianakos VP, Panagiotakos D

Publication type: Article

Publication status: Published

Journal: Health Information Science and Systems

Year: 2022

Volume: 10

Issue: 1

Online publication date: 18/04/2022

Acceptance date: 30/03/2022

ISSN (electronic): 2047-2501

Publisher: Springer

URL: https://doi.org/10.1007/s13755-022-00171-1

DOI: 10.1007/s13755-022-00171-1


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