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Rescuing Relevant Features from Active Aging Surveys: A Data Mining Perspective

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Information and Communication Technologies for Ageing Well and e-Health (ICT4AWE 2021, ICT4AWE 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1856))

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Abstract

Within the psychological currents, several proposals on active aging have been defined, conceptualizing it as a perspective or differentiated way of aging satisfactorily. These proposals generate indicators that assess the level of physical health, psychological well-being and adequate social and spiritual adaptation. The indicators are quantified based on active ageing surveys whose are differs for each ageing proposal and collects different features of active aging such as: health, cognition, activity, affection, fitness, and satisfaction levels. This methodology is focused on rescuing the relevant factors (features) facilitating the interpretation of the data, avoiding the non-required characteristics. The methodology proposes a set of data mining techniques for different types of data that could be present in the forms of active aging, and seeking to make a concrete proposal, the features of an active aging survey are evaluated, determining a subset of features where their weights make them more relevant in data collection, and finally, this methodology is positively evaluated as a model of acceptance by geriatric psychologists.

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Acknowledgements

The authors wish to thank the Vice-Rector for Research of the University of Azuay for the financial and academic support and all the staff of the Laboratory for Research and Development in Informatics (LIDI), and the Department of Computer Science of Universidad de Cuenca.

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Authors and Affiliations

  1. Laboratorio de Investigación y Desarrollo en Informática - LIDI, Universidad del Azuay, Cuenca, Ecuador

    Juan-Fernando Lima, María-Inés Acosta-Urigüen, Marcos Orellana & Alexandra Bueno-Pacheco

  2. Departamento de Ciencias de la Computación, Universidad de Cuenca, Cuenca, Ecuador

    Priscila Cedillo

  3. Facultad de Informática, Universidad Nacional de La Plata, Bueno Aires, Argentina

    María-Inés Acosta-Urigüen

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  1. Juan-Fernando Lima
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  2. Priscila Cedillo
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Correspondence to Marcos Orellana .

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Editors and Affiliations

  1. Wrocław University of Economics Institute of Business Informatics and Macquarie University,, Wroclaw, Poland

    Leszek A. Maciaszek

  2. University of Ulster, Newtownabbey, UK

    Maurice D. Mulvenna

  3. RWTH Aachen University, Aachen, Germany

    Martina Ziefle

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Lima, JF., Cedillo, P., Acosta-Urigüen, MI., Orellana, M., Bueno-Pacheco, A. (2023). Rescuing Relevant Features from Active Aging Surveys: A Data Mining Perspective. In: Maciaszek, L.A., Mulvenna, M.D., Ziefle, M. (eds) Information and Communication Technologies for Ageing Well and e-Health. ICT4AWE ICT4AWE 2021 2022. Communications in Computer and Information Science, vol 1856. Springer, Cham. https://doi.org/10.1007/978-3-031-37496-8_8

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