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WIREs Data Mining Knowl Discov
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A revision and analysis of the comprehensiveness of the main longitudinal studies of human aging for data mining research

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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. WIREs Data Mining Knowl Discov 2017, 7:e1202. doi: 10.1002/widm.1202

Proportion of population by age range. Source: UNDESA
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Percentage of articles using data from each database.
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Percentage of articles analyzing data from each dimension of the proposed taxonomy.
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Percentage of reviewed articles that used each data analysis technique.
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A taxonomy of aspects influencing human aging.
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Algorithmic Development > Spatial and Temporal Data Mining
Fundamental Concepts of Data and Knowledge > Motivation and Emergence of Data Mining

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