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

Barmpas, Petros (author)
Tasoulis, Sotiris (author)
Vrahatis, Aristidis G. (author)
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Georgakopoulos, Spiros V. (author)
Anagnostou, Panagiotis (author)
Prina, Matthew (author)
Ayuso-Mateos, José Luis (author)
Bickenbach, Jerome (author)
Bayes, Ivet (author)
Bobak, Martin (author)
Caballero, Francisco Félix (author)
Chatterji, Somnath (author)
Egea-Cortés, Laia (author)
García-Esquinas, Esther (author)
Leonardi, Matilde (author)
Koskinen, Seppo (author)
Koupil, Ilona (author)
Stockholms universitet,Centrum för forskning om ojämlikhet i hälsa (CHESS),Karolinska Institutet, Sweden
Paja̧k, Andrzej (author)
Prince, Martin (author)
Sanderson, Warren (author)
Scherbov, Sergei (author)
Tamosiunas, Abdonas (author)
Galas, Aleksander (author)
Haro, Josep Maria (author)
Sanchez-Niubo, Albert (author)
Plagianakos, Vassilis P. (author)
Panagiotakos, Demosthenes (author)
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 (creator_code:org_t)
2022-04-18
2022
English.
In: Health Information Science and Systems. - : Springer Science and Business Media LLC. - 2047-2501. ; 10:1
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  • Journal article (peer-reviewed)
Abstract Subject headings
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  • 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).

Subject headings

MEDICIN OCH HÄLSOVETENSKAP  -- Hälsovetenskap -- Folkhälsovetenskap, global hälsa, socialmedicin och epidemiologi (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Health Sciences -- Public Health, Global Health, Social Medicine and Epidemiology (hsv//eng)
NATURVETENSKAP  -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
NATURAL SCIENCES  -- Computer and Information Sciences -- Computer Sciences (hsv//eng)

Keyword

Clustering
Prediction enhancement
ATHLOS cohort
Ensemble methods

Publication and Content Type

ref (subject category)
art (subject category)

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