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

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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).

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Acknowledgements

This work is supported by the ATHLOS (Aging Trajectories of Health: Longitudinal Opportunities and Synergies) project, funded by the European Union’s Horizon 2020 Research and Innovation Program under Grant Agreement Number 635316.

Author information

Authors and Affiliations

  1. Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece

    Petros Barmpas, Sotiris Tasoulis, Aristidis G. Vrahatis, Panagiotis Anagnostou & Vassilis P. Plagianakos

  2. Social Epidemiology Research Group. Health Service and Population Research Department, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK

    Matthew Prina

  3. Global Health Institute, King’s College London, London, UK

    Matthew Prina & Martin Prince

  4. Centro de Investigación Biomédica en Red de Salud Mental, CIBERSAM, Madrid, Spain

    José Luis Ayuso-Mateos, Ivet Bayes, Josep Maria Haro & Albert Sanchez-Niubo

  5. Department of Psychiatry, Universidad Autónoma de Madrid, Madrid, Spain

    José Luis Ayuso-Mateos

  6. Hospital Universitario de La Princesa, Instituto de Investigación Sanitaria Princesa (IIS Princesa), Madrid, Spain

    José Luis Ayuso-Mateos

  7. Swiss Paraplegic Research, Guido A. Zäch Institute (GZI), Nottwil, Switzerland

    Jerome Bickenbach

  8. Department of Health Sciences & Health Policy, University of Lucerne, Lucerne, Switzerland

    Jerome Bickenbach

  9. Department of Epidemiology and Public Health, University College London, London, UK

    Martin Bobak

  10. Department Preventive Medicine and Public Health, Universidad Autónoma de Madrid, Idipaz, Madrid, Spain

    Francisco Félix Caballero & Esther García-Esquinas

  11. Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública, CIBERESP, Madrid, Spain

    Francisco Félix Caballero & Esther García-Esquinas

  12. Information, Evidence and Research, World Health Organization, Geneva, Switzerland

    Somnath Chatterji

  13. Research, Innovation and Teaching Unit. Parc Sanitari Sant Joan de Déu, Sant Boi de Llobregat, Spain

    Ivet Bayes, Laia Egea-Cortés, Josep Maria Haro & Albert Sanchez-Niubo

  14. Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy

    Matilde Leonardi

  15. National Institute for Health and Welfare (THL), Helsinki, Finland

    Seppo Koskinen

  16. Centre for Health Equity Studies, Department of Public Health Sciences, Stockholm University, Stockholm, Sweden

    Ilona Koupil

  17. Department of Public Health Sciences, Karolinska Institutet, Stockholm, Sweden

    Ilona Koupil

  18. Department of Epidemiology and Population Studies, Jagienllonian University, Krakow, Poland

    Andrzej Paja̧k

  19. Centre for Global Mental Health. Health Service and Population Research Department, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK

    Martin Prince

  20. International Institute for Applied Systems Analysis, World Population Program, Wittgenstein Centre for Demography and Global Human Capital, Laxenburg, Austria

    Warren Sanderson & Sergei Scherbov

  21. Department of Economics, Stony Brook University, Stony Brook, NY, USA

    Warren Sanderson

  22. Austrian Academy of Science, Vienna Institute of Demography, Vienna, Austria

    Sergei Scherbov

  23. Russian Presidential Academy of National Economy and Public Administration (RANEPA), Moscow, Russian Federation

    Sergei Scherbov

  24. Lithuanian University of Health Sciences, Kaunas, Lithuania

    Abdonas Tamosiunas

  25. Department of Epidemiology and Preventive Medicine, Jagiellonian University, Krakow, Poland

    Aleksander Galas

  26. Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University, Athens, Greece

    Demosthenes Panagiotakos

  27. Department of Mathematics, University of Thessaly, Lamia, Greece

    Spiros V. Georgakopoulos

Authors
  1. Petros Barmpas
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  2. Sotiris Tasoulis
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  3. Aristidis G. Vrahatis
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  4. Spiros V. Georgakopoulos
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  5. Panagiotis Anagnostou
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  6. Matthew Prina
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  7. José Luis Ayuso-Mateos
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  8. Jerome Bickenbach
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  9. Ivet Bayes
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  10. Martin Bobak
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  11. Francisco Félix Caballero
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  12. Somnath Chatterji
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  13. Laia Egea-Cortés
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  14. Esther García-Esquinas
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  15. Matilde Leonardi
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  16. Seppo Koskinen
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  17. Ilona Koupil
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  18. Andrzej Paja̧k
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  19. Martin Prince
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  20. Warren Sanderson
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  21. Sergei Scherbov
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  22. Abdonas Tamosiunas
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Correspondence to Petros Barmpas.

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Barmpas, P., Tasoulis, S., Vrahatis, A.G. et al. A divisive hierarchical clustering methodology for enhancing the ensemble prediction power in large scale population studies: the ATHLOS project. Health Inf Sci Syst 10, 6 (2022). https://doi.org/10.1007/s13755-022-00171-1

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