Finding Insights between Active Aging Variables: Towards a Data Mining Approach

Several proposals on active aging have been addressed within the psychological field, conceptualizing it satisfactorily as a perspective of aging. Those proposals generate indicators that assess the level of physical health, psychological wellbeing, adequate social adaptation. Physical, cognitive, and functional faculties, interpersonal relationships, and productive activities have been evaluated. Although several technological approaches have been proposed to promote active aging, they have not included a deep understanding of the results obtained from solution implementations. Then, this paper presents the first step towards an approach that uses variables proposed by active aging models (e.g., health, cognition, activity, affection, fitness aspects) to generate knowledge through patterns. These patterns are identified using data obtained through several instruments (i.e., psychological evaluations, health studies, and human experts' contributions). Thus, selecting those variables and evaluating them as future models is necessary. Domain experts perform this evaluation. The evaluation of this proposal has been completed with participants belonging to the health area through a case study. This evaluation generates input data for engineers to apply data mining techniques to reveal strategic knowledge. Finally, from the psychologist's point of view, the results showed that the contribution results are appropriate for achieving healthy aging indicators.