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A revision and analysis of the comprehensiveness of the main longitudinal studies of human ageing for data mining research

Ribeiro, C.E., Brito, L.H.S., Nobre, C.N., Freitas, Alex A., Zarate, L.E. (2017) A revision and analysis of the comprehensiveness of the main longitudinal studies of human ageing for data mining research. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 7 (3). e1202. ISSN 1942-4795. E-ISSN 1942-4795. (doi:10.1002/widm.1202)

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Abstract

Human aging is a global problem that will have a large socioeconomic impact. A better understanding of aging can direct public policies that minimize its negative effects in the future. Over many years, several longitudinal studies of human aging have been conducted aiming to comprehend the phenomenon, and various factors influencing human aging are under analysis. In this review, we categorize the main aspects affecting human aging into a taxonomy for assisting data mining (DM) research on this topic. We also present tables summarizing the main characteristics of 64 research articles using data from aging-related longitudinal studies, in terms of the aging-related aspects analyzed, the main data analysis techniques used, and the specific longitudinal database mined in each article. Finally, we analyze the comprehensiveness of the main databases of longitudinal studies of human aging worldwide, regarding which proportion of the proposed taxonomy's aspects are covered by each longitudinal database. We observed that most articles analyzing such data use classical (parametric, linear) statistical techniques, with little use of more modern (nonparametric, nonlinear) DM methods for analyzing longitudinal databases of human aging. We hope that this article will contribute to DM research in two ways: first, by drawing attention to the important problem of global aging and the free availability of several longitudinal databases of human aging; second, by providing useful information to make research design choices about mining such data, e.g., which longitudinal study and which types of aging-related aspects should be analyzed, depending on the research's goals.

Item Type: Article
DOI/Identification number: 10.1002/widm.1202
Uncontrolled keywords: data mining, machine learning, ageing, aging, longitudinal data
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Divisions: Faculties > Sciences > School of Computing > Computational Intelligence Group
Faculties > Sciences > School of Computing > Data Science
Depositing User: Alex Freitas
Date Deposited: 06 Apr 2017 14:50 UTC
Last Modified: 29 May 2019 18:55 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/61246 (The current URI for this page, for reference purposes)
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