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

Barmpas, P., Tasoulis, S., Vrahatis, A.G., Georgakopoulos, S.V., Anagnostou, P., Prina, M., Ayuso-Mateos, J.L., Bickenbach, J., et al. (2022). A divisive hierarchical clustering methodology for enhancing the ensemble prediction power in large scale population studies: the ATHLOS project. Health Information Science and Systems 10 (1) e6. 10.1007/s13755-022-00171-1.

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Abstract

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).

Item Type:	Article
Uncontrolled Keywords:	Clustering; Prediction enhancement; ATHLOS cohort; Ensemble methods
Research Programs:	Population and Just Societies (POPJUS) Population and Just Societies (POPJUS) > Social Cohesion, Health, and Wellbeing (SHAW)
Depositing User:	Michaela Rossini
Date Deposited:	05 May 2022 13:39
Last Modified:	05 May 2022 13:39
URI:	https://pure.iiasa.ac.at/17986

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